feat: send concurrency metrics to LLM for async optimization

[KRRT7]

Summary

• For async functions, runs the concurrency benchmark before the optimization request so the LLM receives runtime metrics proving the function blocks the event loop
• Sends concurrency_metrics (ratio, sequential/concurrent time) in the /optimize API payload
• Caches the benchmark result to avoid running it twice (reused during baseline establishment)
• Sync function flow unchanged (parallel test-gen + optimization)

Motivation

The e2e-python (async-optimization) test is flaky because the LLM only receives is_async: true — a bare boolean with no context about what to optimize. With a concurrency_ratio ≈ 1.0, the LLM gets concrete evidence that the function doesn't parallelize, steering it toward the correct fix (e.g. time.sleep → asyncio.sleep).

Backend dependency

The backend needs to consume the new concurrency_metrics field from the payload and include it in the LLM prompt. Without that change, this field is safely ignored.

Test plan

• Verify CI prek passes (ruff + mypy on changed lines)
• Unit tests pass (async + optimizer suites)
• Backend consumes concurrency_metrics in prompt construction
• Re-run async e2e test to confirm improved reliability