### 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)

sqlite_where=text("state='running'"),
        ),
        
        
        Index(
            "idx_dag_run_queued_dags",
            "state",
            "dag_id",
            postgresql_where=text("state='queued'"),
            mssql_where=text("state='queued'"),
            sqlite_where=text("state='queued'"),
        ),
    )
    task_instances = relationship(
        TI, back_populates="dag_run", cascade="save-update, merge, delete, delete-orphan"
    )
    dag_model = relationship(
        "DagModel",
        primaryjoin="foreign(DagRun.dag_id) == DagModel.dag_id",
        uselist=False,
        viewonly=True,
    )
    dag_run_note = relationship("DagRunNote", back_populates="dag_run", uselist=False)
    note = association_proxy("dag_run_note", "content", creator=_creator_note)
    DEFAULT_DAGRUNS_TO_EXAMINE = airflow_conf.getint(
        "scheduler",
        "max_dagruns_per_loop_to_schedule",
        fallback=20,
    )
    def __init__(