### 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)

return cls(
            dag_id=ti.dag_id,
            task_id=ti.task_id,
            run_id=ti.run_id,
            map_index=ti.map_index,
            start_date=ti.start_date,
            end_date=ti.end_date,
            try_number=ti.try_number,
            state=ti.state,
            executor_config=ti.executor_config,
            pool=ti.pool,
            queue=ti.queue,
            key=ti.key,
            run_as_user=ti.run_as_user if hasattr(ti, "run_as_user") else None,
            priority_weight=ti.priority_weight if hasattr(ti, "priority_weight") else None,
        )
    @classmethod
    def from_dict(cls, obj_dict: dict) -> SimpleTaskInstance: