feat: add batches API with OpenAI compatibility (with inference replay) (#3162)

Add complete batches API implementation with protocol, providers, and
tests:

Core Infrastructure:
- Add batches API protocol using OpenAI Batch types directly
- Add Api.batches enum value and protocol mapping in resolver
- Add OpenAI "batch" file purpose support
- Include proper error handling (ConflictError, ResourceNotFoundError)

Reference Provider:
- Add ReferenceBatchesImpl with full CRUD operations (create, retrieve,
cancel, list)
- Implement background batch processing with configurable concurrency
- Add SQLite KVStore backend for persistence
- Support /v1/chat/completions endpoint with request validation

Comprehensive Test Suite:
- Add unit tests for provider implementation with validation
- Add integration tests for end-to-end batch processing workflows
- Add error handling tests for validation, malformed inputs, and edge
cases

Configuration:
- Add max_concurrent_batches and max_concurrent_requests_per_batch
options
- Add provider documentation with sample configurations

Test with -

```
$ uv run llama stack build --image-type venv --providers inference=YOU_PICK,files=inline::localfs,batches=inline::reference --run &
$ LLAMA_STACK_CONFIG=http://localhost:8321 uv run pytest tests/unit/providers/batches tests/integration/batches --text-model YOU_PICK
```

addresses #3066

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

llama_stack/distributions/starter/starter.py

@@ -139,6 +139,9 @@ def get_distribution_template() -> DistributionTemplate:
             BuildProvider(provider_type="inline::rag-runtime"),
             BuildProvider(provider_type="remote::model-context-protocol"),
         ],
+        "batches": [
+            BuildProvider(provider_type="inline::reference"),
+        ],
     }
     files_provider = Provider(
         provider_id="meta-reference-files",