Add deferrable support to LLM operators in common.ai provider

[guan404ming]

Why

LLM calls in LLMOperator / AgentOperator block a worker slot for the whole API call (30s to minutes). Airflow's deferral mechanism fits this perfectly.

Proposal

Step 1 — LLMTrigger + deferrable LLMOperator

• Run the LLM call in the triggerer via async agent.run(), freeing the worker slot
• deferrable=True on LLMOperator, rehydrate output_type on resume
• Chain with require_approval (HITL) so the whole flow never holds a worker

Step 2 — Deferrable subclasses

• LLMBranchOperator (branch on resume)
• LLMSQLOperator, LLMSchemaCompareOperator

Step 3 — AgentTrigger for AgentOperator

• Multi-turn agent in the triggerer, message history serialized
• Toolsets rebuilt from serializable spec (conn_id + hook path)
• Durable-storage replay on triggerer restart, no re-billed LLM calls

Use case

• Hundreds of concurrent LLM tasks without holding hundreds of worker slots
• Long-running reasoning/agent tasks (10+ min) become cheap to run