### 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)

partial_dag = task.dag.partial_subset(
                task.downstream_task_ids,
                include_downstream=True,
                include_upstream=False,
                include_direct_upstream=True,
            )
            dag_run.dag = partial_dag
            info = dag_run.task_instance_scheduling_decisions(session)
            skippable_task_ids = {
                task_id for task_id in partial_dag.task_ids if task_id not in task.downstream_task_ids
            }
            schedulable_tis = [ti for ti in info.schedulable_tis if ti.task_id not in skippable_task_ids]
            for schedulable_ti in schedulable_tis:
                if not hasattr(schedulable_ti, "task"):
                    schedulable_ti.task = task.dag.get_task(schedulable_ti.task_id)
            num = dag_run.schedule_tis(schedulable_tis, session=session)
            self.log.info("%d downstream tasks scheduled from follow-on schedule check", num)
            session.flush()
        except OperationalError as e:
            
            self.log.info(
                "Skipping mini scheduling run due to exception: %s",
                e.statement,
                exc_info=True,
            )
            session.rollback()
    def get_relevant_upstream_map_indexes(