### 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)

try:
            paused_runs = (
                session.query(DagRun)
                .join(DagRun.dag_model)
                .join(TI)
                .filter(
                    DagModel.is_paused == expression.true(),
                    DagRun.state == DagRunState.RUNNING,
                    DagRun.run_type != DagRunType.BACKFILL_JOB,
                )
                .having(DagRun.last_scheduling_decision <= func.max(TI.updated_at))
                .group_by(DagRun)
            )
            for dag_run in paused_runs:
                dag = self.dagbag.get_dag(dag_run.dag_id, session=session)
                if dag is None:
                    continue
                dag_run.dag = dag
                _, callback_to_run = dag_run.update_state(execute_callbacks=False, session=session)
                if callback_to_run:
                    self._send_dag_callbacks_to_processor(dag, callback_to_run)
        except Exception as e:  
            self.log.exception("Failed to update dag run state for paused dags due to %s", str(e))
    def _run_scheduler_loop(self) -> None:
        """
        The actual scheduler loop.
        The main steps in the loop are: