[Bugfix] Check only prompt tail for reasoning end in DelegatingParser

[pynterest83]

DelegatingParser.parse_delta scanned the entire prompt with is_reasoning_end(), so any reasoning marker inside the chat template or an earlier turn marked reasoning as already finished and the whole generation was emitted as content.

Check only the last tokens of the prompt instead, sized to the parser's end marker.

Purpose

DelegatingParser.parse_delta calls is_reasoning_end(prompt_token_ids) over the
entire prompt. Any reasoning marker present in the prompt -- from chat-template
instructions, few-shot examples, or an earlier assistant turn -- makes it conclude
reasoning already ended, so state.reasoning_ended is set before the first
generated token and the whole response is emitted as content.

Reproduced with MiniMax-M3 (--reasoning-parser minimax_m3) on v0.25.1 and still
present on main. The rendered prompt contains three markers from the model's own
<thinking_instructions> block, at offsets 394 (<mm:think>), 404 and 509
(</mm:think>). rfind(end) > rfind(start) -> True -> reasoning phase skipped.

Related: #46042 (same symptom). #46663 fixes the equivalent check in
OpenAIServingChat, but that fix alone is not sufficient: parse_delta
recomputes the value independently. Verified on a deployment already carrying
#46663 -- the bug persisted until this second site was fixed.

Test Plan

Added tests/parser/engine/test_prompt_reasoning_end.py:

pytest tests/parser/engine/test_prompt_reasoning_end.py -v

End-to-end: MiniMax-M3-NVFP4, 4x H200, TP=4, streaming chat completions.

Test Result

Unit tests: 5 passed with the fix; 4 fail without it.

End-to-end before the fix: every streamed response emitted a literal <mm:think>
delta inside content and no reasoning_content field was present.
After the fix: reasoning is correctly separated into reasoning_content.

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[claude]

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[bbrowning]

The proper is_reasoning_end logic is model-specific, and the right place to fix this for any specific situation would be in that model's reasoning parser instead of a global change across all models like this.