### 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)

self,
        task: Operator,
        execution_date: datetime | None = None,
        run_id: str | None = None,
        state: str | None = None,
        map_index: int = -1,
    ):
        super().__init__()
        self.dag_id = task.dag_id
        self.task_id = task.task_id
        self.map_index = map_index
        self.refresh_from_task(task)
        
        self.init_on_load()
        if run_id is None and execution_date is not None:
            from airflow.models.dagrun import DagRun  
            warnings.warn(
                "Passing an execution_date to `TaskInstance()` is deprecated in favour of passing a run_id",
                RemovedInAirflow3Warning,
                
                stacklevel=4,
            )
            
            if execution_date and not timezone.is_localized(execution_date):
                self.log.warning(
                    "execution date %s has no timezone information. Using default from dag or system",
                    execution_date,
                )
                if self.task.has_dag():