### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

Our scheduler started failing with this trace:

  File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1187, in get_failed_dep_statuses
    for dep_status in dep.get_dep_statuses(self, session, dep_context):
  File "/usr/local/lib/python3.9/site-packages/airflow/ti_deps/deps/base_ti_dep.py", line 95, in get_dep_statuses
    yield from self._get_dep_statuses(ti, session, dep_context)
  File "/usr/local/lib/python3.9/site-packages/airflow/ti_deps/deps/not_in_retry_period_dep.py", line 47, in _get_dep_statuses
    next_task_retry_date = ti.next_retry_datetime()
  File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1243, in next_retry_datetime
    return self.end_date + delay
OverflowError: date value out of range

We found a dag with a large # of retries and exponential backoff will trigger this date error and take down the entire scheduler. The workaround is to force a max_delay setting. 

The bug is here:
https://github.com/apache/airflow/blob/2.3.3/airflow/models/taskinstance.py#L1243

The current version seems to use the same code:
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1147


### What you think should happen instead

There are a few solutions. Exponential backoff should probably require a max delay value.

At the very least, it shouldn't kill the scheduler.

### How to reproduce

Create dag with exponential delay and force it to retry until it overflows.

### Operating System

linux

### Versions of Apache Airflow Providers

_No response_

### Deployment

Astronomer

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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)

)
            for dag_id, run_ids in task_id_by_key.items()
        )
        delete_qry = TR.__table__.delete().where(conditions)
        session.execute(delete_qry)
    if job_ids:
        from airflow.jobs.base_job import BaseJob
        for job in session.query(BaseJob).filter(BaseJob.id.in_(job_ids)).all():
            job.state = TaskInstanceState.RESTARTING
    if activate_dag_runs is not None:
        warnings.warn(
            "`activate_dag_runs` parameter to clear_task_instances function is deprecated. "
            "Please use `dag_run_state`",
            RemovedInAirflow3Warning,
            stacklevel=2,
        )
        if not activate_dag_runs:
            dag_run_state = False
    if dag_run_state is not False and tis:
        from airflow.models.dagrun import DagRun  
        run_ids_by_dag_id = defaultdict(set)
        for instance in tis:
            run_ids_by_dag_id[instance.dag_id].add(instance.run_id)
        drs = (
            session.query(DagRun)
            .filter(
                or_(
                    and_(DagRun.dag_id == dag_id, DagRun.run_id.in_(run_ids))
                    for dag_id, run_ids in run_ids_by_dag_id.items()
                )