[OpenAI] Fix reasoning_tokens accounting for MiniMax M2 chat completions

[QwertyJack]

Summary

• add completion_tokens_details.reasoning_tokens to OpenAI chat-completions usage
• count MiniMax M2 reasoning tokens using the first </think> token as the reasoning boundary for both minimax_m2 and minimax_m2_append_think
• cover non-streaming and streaming usage reporting with targeted unit tests
• mark the new pure unit tests with skip_global_cleanup because they do not allocate accelerator state and do not need global cleanup

Testing

• python -m pytest -q tests/entrypoints/openai/chat_completion/test_serving_chat.py -k 'test_chat_usage_includes_reasoning_tokens_for_minimax_parser or test_chat_stream_usage_includes_reasoning_tokens_for_minimax_parser'
• python -m pytest -q tests/reasoning/test_minimax_m2_reasoning_parser.py tests/reasoning/test_minimax_m2_append_reasoning_parser.py -k count_reasoning_tokens

[gemini-code-assist]

Code Review

This pull request introduces the capability to track and report 'reasoning tokens' as part of the usage information for chat completions, specifically for Minimax M2 models. This involves defining a new CompletionTokenUsageInfo data structure, implementing the logic to count reasoning tokens within the MiniMaxM2ReasoningParser and MiniMaxM2AppendThinkReasoningParser classes, and integrating this counting into the OpenAIServingChat class for both full and streaming chat completions. New unit tests were added to verify the correct calculation and reporting of reasoning tokens for these parsers.

[mergify]

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @QwertyJack.

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