### 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)

"""
        Unconditionally create a DAG run for the given DAG, and update the dag_model's fields to control
        if/when the next DAGRun should be created
        """
        
        
        
        
        existing_dagruns = (
            session.query(DagRun.dag_id, DagRun.execution_date)
            .filter(
                tuple_in_condition(
                    (DagRun.dag_id, DagRun.execution_date),
                    ((dm.dag_id, dm.next_dagrun) for dm in dag_models),
                ),
            )
            .all()
        )
        active_runs_of_dags = Counter(
            DagRun.active_runs_of_dags(dag_ids=(dm.dag_id for dm in dag_models), session=session),
        )
        for dag_model in dag_models:
            dag = self.dagbag.get_dag(dag_model.dag_id, session=session)