Add token usage tracking to generate_schema / agenerate_schema

generate_schema can make up to 5 internal LLM calls (field inference,
schema generation, validation retries) with no way to track token
consumption. Add an optional `usage: TokenUsage = None` parameter that
accumulates prompt/completion/total tokens across all calls in-place.

- _infer_target_json: accept and populate usage accumulator
- agenerate_schema: track usage after every aperform_completion call
  in the retry loop, forward usage to _infer_target_json
- generate_schema (sync): forward usage to agenerate_schema

Fully backward-compatible — omitting usage changes nothing.

docs/md_v2/complete-sdk-reference.md

@@ -4537,6 +4537,20 @@ xpath_schema = JsonXPathExtractionStrategy.generate_schema(
 # Use the generated schema for fast, repeated extractions
 strategy = JsonCssExtractionStrategy(css_schema)
 ```
+### Token Usage Tracking
+`generate_schema` may make multiple LLM calls internally (field inference, generation, validation retries). Track total token consumption by passing a `TokenUsage` accumulator:
+```python
+from crawl4ai.models import TokenUsage
+
+usage = TokenUsage()
+schema = JsonCssExtractionStrategy.generate_schema(
+    url="https://example.com/products",
+    query="extract product name and price",
+    usage=usage,
+)
+print(f"Total tokens: {usage.total_tokens}")
+```
+The `usage` parameter is optional and fully backward-compatible. Both `generate_schema` (sync) and `agenerate_schema` (async) support it.
 ### LLM Provider Options
 1. **OpenAI GPT-4 (`openai/gpt4o`)**
    - Default provider