### 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)

"""
    DagRun describes an instance of a Dag. It can be created
    by the scheduler (for regular runs) or by an external trigger
    """
    __tablename__ = "dag_run"
    id = Column(Integer, primary_key=True)
    dag_id = Column(StringID(), nullable=False)
    queued_at = Column(UtcDateTime)
    execution_date = Column(UtcDateTime, default=timezone.utcnow, nullable=False)
    start_date = Column(UtcDateTime)
    end_date = Column(UtcDateTime)
    _state = Column("state", String(50), default=State.QUEUED)
    run_id = Column(StringID(), nullable=False)
    creating_job_id = Column(Integer)
    external_trigger = Column(Boolean, default=True)
    run_type = Column(String(50), nullable=False)
    conf = Column(PickleType)
    
    data_interval_start = Column(UtcDateTime)