[CI] Some improvements to Nightly reports summaries

[DN6]

What does this PR do?

We currently create summary reports for each test module, but the number of pipelines we test in the nightlys has grown considerably. Scrolling through them all is starting to get challenging. This PR introduces an additional step that consolidates the individual reports into a single report with some useful summary information.

A shorter summary report is also sent to the diffusers-ci Slack Channel with a link to the full report in Github Actions.

Example report below

# Diffusers Nightly Test Report
Generated on: 2025-03-28 12:04:31

## Summary
|:---------------|:---------|
| Total Tests    | 2429     |
| Passed         | 2121     |
| Failed         | 11       |
| Skipped        | 293      |
| Success Rate   | 87.32%   |
| Total Duration | 1768.28s |

## Test Suites
| Test Suite                                                  |   Tests |   Passed |   Failed |   Skipped | Success Rate   |   Duration (s) |
|:------------------------------------------------------------|--------:|---------:|---------:|----------:|:---------------|---------------:|
| torch_models_cuda/tests_torch_models_cuda                   |    2016 |     1729 |        6 |       277 | 85.76%         |         576.82 |
| torch_minimum_version_cuda/tests_torch_minimum_version_cuda |     250 |      235 |        3 |        12 | 94.00%         |         498.16 |
| pipeline_cogvideo/tests_pipeline_cogvideo_cuda              |     135 |      129 |        2 |         4 | 95.56%         |         332.4  |
| torch_cuda_gguf_reports/tests_gguf_torch_cuda               |      28 |       28 |        0 |         0 | 100.00%        |         360.9  |

## Slowest Tests
|   Rank | Test                                                                                                                     |   Duration (s) | Test Suite                                                  |
|-------:|:-------------------------------------------------------------------------------------------------------------------------|---------------:|:------------------------------------------------------------|
|      1 | tests/pipelines/test_pipelines_auto.py::AutoPipelineIntegrationTest::test_from_pipe_consistent                           |         156.63 | torch_minimum_version_cuda/tests_torch_minimum_version_cuda |
|      2 | tests/pipelines/cogvideo/test_cogvideox_image2video.py::CogVideoXImageToVideoPipelineIntegrationTests::test_cogvideox    |         139.11 | pipeline_cogvideo/tests_pipeline_cogvideo_cuda              |
|      3 | tests/pipelines/cogvideo/test_cogvideox.py::CogVideoXPipelineIntegrationTests::test_cogvideox                            |         106.64 | pipeline_cogvideo/tests_pipeline_cogvideo_cuda              |
|      4 | tests/quantization/gguf/test_gguf.py::SD35MediumGGUFSingleFileTests::test_pipeline_inference                             |          81.41 | torch_cuda_gguf_reports/tests_gguf_torch_cuda               |
|      5 | tests/pipelines/test_pipelines.py::PipelineNightlyTests::test_ddpm_ddim_equality_batched                                 |          80.82 | torch_minimum_version_cuda/tests_torch_minimum_version_cuda |
|      6 | tests/quantization/gguf/test_gguf.py::SD35LargeGGUFSingleFileTests::test_pipeline_inference                              |          76.58 | torch_cuda_gguf_reports/tests_gguf_torch_cuda               |
|      7 | tests/quantization/gguf/test_gguf.py::FluxGGUFSingleFileTests::test_pipeline_inference                                   |          54.15 | torch_cuda_gguf_reports/tests_gguf_torch_cuda               |
|      8 | tests/pipelines/test_pipelines.py::PipelineSlowTests::test_weighted_prompts_compel                                       |          40.98 | torch_minimum_version_cuda/tests_torch_minimum_version_cuda |
|      9 | tests/models/autoencoders/test_models_consistency_decoder_vae.py::ConsistencyDecoderVAEIntegrationTests::test_vae_tiling |          34.08 | torch_models_cuda/tests_torch_models_cuda                   |
|     10 | tests/pipelines/test_pipelines_auto.py::AutoPipelineIntegrationTest::test_controlnet                                     |          30.95 | torch_minimum_version_cuda/tests_torch_minimum_version_cuda |

## Failures
### AutoPipelineIntegrationTest
tests/pipelines/test_pipelines_auto.py::AutoPipelineIntegrationTest::test_from_pipe_consistent - ValueError: You are trying to load model files of the `variant=fp16`, but no such modeling files are available.
tests/pipelines/test_pipelines_auto.py::AutoPipelineIntegrationTest::test_pipe_auto - ValueError: You are trying to load model files of the `variant=fp16`, but no such modeling files are available.

### AutoencoderOobleckIntegrationTests
tests/models/autoencoders/test_models_autoencoder_oobleck.py::AutoencoderOobleckIntegrationTests::test_stable_diffusion_0 - ImportError: Numba needs NumPy 2.1 or less. Got NumPy 2.2.
tests/models/autoencoders/test_models_autoencoder_oobleck.py::AutoencoderOobleckIntegrationTests::test_stable_diffusion_1 - ImportError: Numba needs NumPy 2.1 or less. Got NumPy 2.2.
tests/models/autoencoders/test_models_autoencoder_oobleck.py::AutoencoderOobleckIntegrationTests::test_stable_diffusion_encode_decode_0 - ImportError: Numba needs NumPy 2.1 or less. Got NumPy 2.2.
tests/models/autoencoders/test_models_autoencoder_oobleck.py::AutoencoderOobleckIntegrationTests::test_stable_diffusion_encode_decode_1 - ImportError: Numba needs NumPy 2.1 or less. Got NumPy 2.2.
tests/models/autoencoders/test_models_autoencoder_oobleck.py::AutoencoderOobleckIntegrationTests::test_stable_diffusion_mode - ImportError: Numba needs NumPy 2.1 or less. Got NumPy 2.2.

Fixes # (issue)

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[sayakpaul]

Good initiative. Can we see an example message on Slack?

Also, the benefit of the previous action was it pointed to the specific action run of which the failing tests are a part of. Are we doing that in this PR? If not, I emphasize on including that part.