[Feat] Support async_chunk additional_information delivery to V2 model runner

[Sy0307]

Purpose

Fix async_chunk mode producing garbage/short audio in V2 model runner.

Root cause: additional_information (containing thinker_decode_embeddings and thinker_output_token_ids) was never propagated from the scheduler's CachedRequestData to the runner's intermediate_buffer during decode steps. The chunk_transfer_adapter correctly polled data from SharedMemoryConnector and attached it to scheduled_cached_reqs.additional_information, but GPUModelRunner.update_requests() does not handle this field — so the data was silently dropped.

Additionally fixes three correctness issues found during review:

1. _handle_async_chunk_updates passed raw AdditionalInformationPayload objects to intermediate_buffer.update(), which expects dict — causing AttributeError when payload is not pre-resolved
2. Inline deserialization in scheduler only preserved list_data, silently dropping tensor_data and scalar_data entries
3. cleanup() (sender+receiver) replaced cleanup_receiver() in the failed-KV-load path, risking race conditions with background save threads

Test Plan

• Qwen3-Omni-30B async_chunk end-to-end (use_audio query, 2xH20)
• ASR verification: Whisper transcription matches expected text output
• Audio duration: 22.04s (previously 4.57s with the bug)
• Sync mode regression check (non-async_chunk path unchanged)

Test Result

Before fix: Talker sees thinker_output_token_ids=[], thinker_decode_embeddings=None -> early EOS after ~33 decode steps -> 4.57s noise audio

After fix: Talker correctly receives incremental thinker data -> 329+ decode steps -> 22.04s audio, ASR output:

"The audio contains a man reciting the nursery rhyme Mary had a little lamb. He begins by saying the first words I spoke in the original phonograph before reciting the rhyme. Mary had a little lamb. Its fleece was white as snow, and everywhere that Mary went, the lamb was sure to go."