old semconv attribute names (prompt_tokens vs input_tokens) — cross-SDK aggregation gap

A follow-up to #1445 (Anthropic cache tokens), from the same 30-project conformance audit.

**The observation**

AgentOps uses the older GenAI semconv attribute names in `agentops/semconv/span_attributes.py` (~L61-63):
```python
LLM_USAGE_PROMPT_TOKENS = "gen_ai.usage.prompt_tokens"
LLM_USAGE_COMPLETION_TOKENS = "gen_ai.usage.completion_tokens"
```

The current OTel GenAI semantic conventions use `gen_ai.usage.input_tokens` / `gen_ai.usage.output_tokens`. OpenLLMetry, pydantic-ai, and other instrumentation we audited have already migrated to the new names.

The file header (~L8-24) contains TODO comments acknowledging mapping issues.

**The consequence**

In an OTLP collector receiving spans from multiple SDKs:
- `sum(gen_ai.usage.input_tokens)` misses all AgentOps spans (they emit `prompt_tokens`)
- `sum(gen_ai.usage.prompt_tokens)` misses all OpenLLMetry spans (they emit `input_tokens`)
- Cross-SDK token aggregation requires knowing which attribute name each SDK uses — per-SDK knowledge that generic consumers shouldn't need

**The question**

Is there a plan to migrate to the current `gen_ai.usage.input_tokens` / `output_tokens` names, or to emit both during a transition? The old names still work within the AgentOps ecosystem, but any downstream tool consuming generic OTel GenAI attributes will silently undercount when AgentOps spans are mixed in.

Happy to send a two-SDK repro (one AgentOps span + one OpenLLMetry span in the same collector, asserting the aggregation gap).

Context: AgentMeasure conformance check; this is the AgentOps follow-up after the Anthropic cache-token finding in #1445.