### 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)

"""
    Simplified Task Instance.
    Used to send data between processes via Queues.
    """
    def __init__(
        self,
        dag_id: str,
        task_id: str,
        run_id: str,
        start_date: datetime | None,
        end_date: datetime | None,
        try_number: int,
        map_index: int,
        state: str,
        executor_config: Any,
        pool: str,
        queue: str,
        key: TaskInstanceKey,
        run_as_user: str | None = None,
        priority_weight: int | None = None,
    ):
        self.dag_id = dag_id
        self.task_id = task_id
        self.run_id = run_id
        self.map_index = map_index