### 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)

warnings.warn(
            "This method is deprecated. Use BaseSerialization.deserialize.",
            RemovedInAirflow3Warning,
            stacklevel=2,
        )
        ti_key = TaskInstanceKey(*obj_dict.pop("key"))
        start_date = None
        end_date = None
        start_date_str: str | None = obj_dict.pop("start_date")
        end_date_str: str | None = obj_dict.pop("end_date")
        if start_date_str:
            start_date = timezone.parse(start_date_str)
        if end_date_str:
            end_date = timezone.parse(end_date_str)
        return cls(**obj_dict, start_date=start_date, end_date=end_date, key=ti_key)
class TaskInstanceNote(Base):