feat: M3.8 query optimizer (7 tests, ready to wire)

QueryOptimizer implements query-time optimization:
- Async optimize_chunk(chunk) before LLM processing
- Batch optimize_chunks() for multiple results
- Graceful fallback: original on optimization failure
- Metrics tracking for cache alignment analysis

Features:
✓ Content-type inference (JSON/logs/diffs/text)
✓ Environment-driven configuration
✓ Optional service integration
✓ Batch processing support
✓ Metrics calculation

Tests (7 passing):
- Disabled optimizer behavior
- Environment variable handling
- Async chunk optimization
- Content-type inference (JSON, logs, diffs, text)
- Metrics calculation

Build: ✅ mem-core (137 tests total, 7 new)

Ready to wire:
1. Ingest path: optimize_record_with_metrics() in rebuild.rs
2. Query path: QueryOptimizer.optimize_chunks() before LLM context

Architecture:
  Ingest: Content → M3.8 compress → clean → embed + index
  Query:  Search → M3.8 optimize → clean → LLM context

Next: Wire into rebuild.rs and query_executor.rs

crates/mem-core/src/optimizer/mod.rs

@@ -13,6 +13,7 @@ pub mod cache_align;
 pub mod ccr;
 pub mod plugin;
 pub mod builtin;
+pub mod query_optimizer;
 
 use anyhow::Result;
 use serde::{Deserialize, Serialize};
@@ -31,6 +32,7 @@ pub use plugin::{
 pub use builtin::{
     BuiltinOptimizer, JsonFormatter, JsonlFormatter, RawFormatter, CsvFormatter, YamlFormatter,
 };
+pub use query_optimizer::{QueryOptimizer, QueryOptimizationMetrics};
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct OptimizedChunk {