### 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)

schedule_delay,
                    tags={"dag_id": dag.dag_id},
                )
        for dag_run in dag_runs:
            dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session)
            if not dag:
                self.log.error("DAG '%s' not found in serialized_dag table", dag_run.dag_id)
                continue
            active_runs = active_runs_of_dags[dag_run.dag_id]
            if dag.max_active_runs and active_runs >= dag.max_active_runs:
                self.log.debug(
                    "DAG %s already has %d active runs, not moving any more runs to RUNNING state %s",
                    dag.dag_id,
                    active_runs,
                    dag_run.execution_date,
                )
            else:
                active_runs_of_dags[dag_run.dag_id] += 1
                _update_state(dag, dag_run)
                dag_run.notify_dagrun_state_changed()
    @retry_db_transaction
    def _schedule_all_dag_runs(self, guard, dag_runs, session):
        """Makes scheduling decisions for all `dag_runs`"""
        callback_tuples = []
        for dag_run in dag_runs:
            callback_to_run = self._schedule_dag_run(dag_run, session)
            callback_tuples.append((dag_run, callback_to_run))
        guard.commit()
        return callback_tuples
    def _schedule_dag_run(