### Description

expanding tasks in batches to allow mapped tasks spawn more than 1024 processes.

### Use case/motivation

Maximum length of a list is limited to 1024 by `max_map_length (AIRFLOW__CORE__MAX_MAP_LENGTH)`.
during scheduling of the new tasks, an UPDATE query is ran that tries to set all the new tasks at once.  Increasing `max_map_length` more than 4K makes airflow scheduler completely unresponsive.

Also, Postgres throws `stack depth limit exceeded` error which can be fixed by updating to a newer version and setting `max_stack_depth` higher. But it doesn't really matter because airflow scheduler freezes up.
 
As a workaround, I split the dag runs into subdag runs which works but it would be much nicer if we didn't have to worry about exceeding  `max_map_length`. 

### Related issues

It was discussed here:
[Increasing 'max_map_length' leads to SQL 'max_stack_depth' error with 5000 dags to be spawned #28478](https://github.com/apache/airflow/discussions/28478)

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""Respond to executor events."""
        if not self._standalone_dag_processor and not self.processor_agent:
            raise ValueError("Processor agent is not started.")
        ti_primary_key_to_try_number_map: dict[tuple[str, str, str, int], int] = {}
        event_buffer = self.job.executor.get_event_buffer()
        tis_with_right_state: list[TaskInstanceKey] = []
        
        for ti_key, value in event_buffer.items():
            state: str
            state, _ = value
            
            ti_primary_key_to_try_number_map[ti_key.primary] = ti_key.try_number
            self.log.info(
                "Executor reports execution of %s.%s run_id=%s exited with status %s for try_number %s",
                ti_key.dag_id,
                ti_key.task_id,
                ti_key.run_id,
                state,
                ti_key.try_number,
            )
            if state in (State.FAILED, State.SUCCESS, State.QUEUED):
                tis_with_right_state.append(ti_key)
        
        if not tis_with_right_state:
            return len(event_buffer)
        
        filter_for_tis = TI.filter_for_tis(tis_with_right_state)
        query = session.query(TI).filter(filter_for_tis).options(selectinload("dag_model"))