LiteLLM integration does not report cached, reasoning, or cache-write token usage

### How do you use Sentry?

Sentry Saas (sentry.io)

### Version

2.52.0

### Steps to Reproduce

1. Initialize Sentry with the LiteLLM integration and tracing enabled
2. Make a completion call through LiteLLM to a provider that supports prompt caching (e.g., OpenAI, Anthropic, etc.)
3. Inspect the resulting span data in Sentry's AI Agents dashboard

### Expected Result

The span should include all available token usage detail attributes, just like [the OpenAI and Anthropic integrations do](https://github.com/getsentry/sentry-python/blob/5a8f060965e8a9ecb63a51ddc072a92ec4e3922b/sentry_sdk/integrations/openai.py#L165-L178):
- `gen_ai.usage.input_tokens` (total input tokens)
- `gen_ai.usage.input_tokens.cached` (cached input tokens, subset of total)
- `gen_ai.usage.input_tokens.cache_write` (cache write tokens, if available)
- `gen_ai.usage.output_tokens` (total output tokens)
- `gen_ai.usage.output_tokens.reasoning` (reasoning tokens, subset of total)
- `gen_ai.usage.total_tokens`

This data is necessary for Sentry to correctly calculate model costs using the formula documented [here](https://docs.sentry.io/product/insights/ai/agents/costs/):
```
input cost = (input_tokens - cached_tokens) x input_rate + cached_tokens x cached_rate
```

Without cached/reasoning token breakdown, all tokens are charged at the full standard rate, producing inaccurate cost estimates.

### Actual Result

The LiteLLM integration's _success_callback only extracts [three basic fields](https://github.com/getsentry/sentry-python/blob/5a8f060965e8a9ecb63a51ddc072a92ec4e3922b/sentry_sdk/integrations/litellm.py#L225-L230):
```python
record_token_usage(
span,
input_tokens=getattr(usage, "prompt_tokens", None),
output_tokens=getattr(usage, "completion_tokens", None),
total_tokens=getattr(usage, "total_tokens", None),
)
```

The `input_tokens_cached`, `input_tokens_cache_write`, and `output_tokens_reasoning` parameters of `record_token_usage()` are never passed. Therefore, cost calculations in the AI Agents dashboard overestimate costs for cached-heavy workloads (all input tokens billed at the full rate) and misattribute output vs. reasoning token costs.