### 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)

existing_dagruns: set[tuple[str, timezone.DateTime]] = set(
            session.query(DagRun.dag_id, DagRun.execution_date).filter(
                tuple_in_condition((DagRun.dag_id, DagRun.execution_date), exec_dates.items())
            )
        )
        for dag_model in dag_models:
            dag = self.dagbag.get_dag(dag_model.dag_id, session=session)
            if not dag:
                self.log.error("DAG '%s' not found in serialized_dag table", dag_model.dag_id)
                continue
            if not isinstance(dag.timetable, DatasetTriggeredTimetable):
                self.log.error(
                    "DAG '%s' was dataset-scheduled, but didn't have a DatasetTriggeredTimetable!",
                    dag_model.dag_id,
                )
                continue
            dag_hash = self.dagbag.dags_hash.get(dag.dag_id)
            
            
            
            
            
            
            
            
            exec_date = exec_dates[dag.dag_id]
            if (dag.dag_id, exec_date) not in existing_dagruns:
                previous_dag_run = (
                    session.query(DagRun)
                    .filter(