### 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)

"""
        Get the concurrency maps.
        :param states: List of states to query for
        :return: Concurrency map
        """
        ti_concurrency_query: list[tuple[str, str, str, int]] = (
            session.query(TI.task_id, TI.run_id, TI.dag_id, func.count("*"))
            .filter(TI.state.in_(states))
            .group_by(TI.task_id, TI.run_id, TI.dag_id)
        )
        return ConcurrencyMap.from_concurrency_map(
            {(dag_id, run_id, task_id): count for task_id, run_id, dag_id, count in ti_concurrency_query}
        )
    def _executable_task_instances_to_queued(self, max_tis: int, session: Session) -> list[TI]: