### 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)

"""Executes Task (optionally with a Timeout) and pushes Xcom results"""
        task_to_execute = self.task
        
        
        
        if self.next_method:
            
            
            if self.next_method == "__fail__":
                next_kwargs = self.next_kwargs or {}
                traceback = self.next_kwargs.get("traceback")
                if traceback is not None:
                    self.log.error("Trigger failed:\n%s", "\n".join(traceback))
                raise TaskDeferralError(next_kwargs.get("error", "Unknown"))
            
            execute_callable = getattr(task_to_execute, self.next_method)
            if self.next_kwargs:
                execute_callable = partial(execute_callable, **self.next_kwargs)
        else:
            execute_callable = task_to_execute.execute
        
        
        if task_to_execute.execution_timeout: