### 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)

ondelete="CASCADE",
        ),
        ForeignKeyConstraint(
            [dag_id, run_id],
            ["dag_run.dag_id", "dag_run.run_id"],
            name="task_instance_dag_run_fkey",
            ondelete="CASCADE",
        ),
    )
    dag_model = relationship(
        "DagModel",
        primaryjoin="TaskInstance.dag_id == DagModel.dag_id",
        foreign_keys=dag_id,
        uselist=False,
        innerjoin=True,
        viewonly=True,
    )
    trigger = relationship("Trigger", uselist=False, back_populates="task_instance")
    triggerer_job = association_proxy("trigger", "triggerer_job")
    dag_run = relationship("DagRun", back_populates="task_instances", lazy="joined", innerjoin=True)
    rendered_task_instance_fields = relationship("RenderedTaskInstanceFields", lazy="noload", uselist=False)
    execution_date = association_proxy("dag_run", "execution_date")
    task_instance_note = relationship("TaskInstanceNote", back_populates="task_instance", uselist=False)
    note = association_proxy("task_instance_note", "content", creator=_creator_note)
    task: Operator  
    is_trigger_log_context: bool = False
    """Indicate to FileTaskHandler that logging context should be set up for trigger logging.
    :meta private:
    """
    def __init__(