feat: add ProcessPoolExecutor for parallel audio analysis

Commit notes

Scaling improvements to Python Essentia service:

## ProcessPoolExecutor Architecture - Each analysis runs in a separate process (bypasses Python GIL) - Pool size = CPU cores - 1 (leave headroom for main process) - Workers are pre-initialized with Essentia algorithms on startup - Cross-process serialization via Pydantic model_dump()

## Parallel Batch Processing - /analyze/batch endpoint processes files concurrently - Semaphore-based concurrency control (configurable max_concurrent) - Maintains original file order in results - Graceful error handling per file

## Non-blocking Async - run_in_executor() ensures event loop isn't blocked - FastAPI can handle multiple concurrent requests - Single uvicorn worker (parallelism via ProcessPoolExecutor)

## New Endpoints - GET /stats - Pool status, worker count, CPU count

## Configuration - ESSENTIA_WORKERS - Number of analysis workers (default: cpu_count - 1) - max_concurrent query param for batch control

This matches the scaling pattern of the Bun worker's WorkerPool but uses multiprocessing (true parallelism) instead of worker threads.

Co-authored-by: armin.naimi <[email redacted]>