[2/n][torchtune integration] implement job management and return training artifacts

[SLR722]

Context

In this PR, we

• Implement the post training job management and get training artifacts apis
  • get_training_jobs
  • get_training_job_status
  • get_training_job_artifacts
  • get_training_job_logstream is deleted since the trace can be directly accessed by UI with Jaeger https://llama-stack.readthedocs.io/en/latest/building_applications/telemetry.html#jaeger-to-visualize-traces
• Refactor the post training and training types definition to make them more intuitive.
• Rewrite the checkpointer to make it compatible with llama-stack file system and can be recognized during inference

Test

Unit test
pytest llama_stack/providers/tests/post_training/test_post_training.py -m "torchtune_post_training_huggingface_datasetio" -v -s --tb=short --disable-warnings

e2e test with client side call