### 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)

os.environ.update(airflow_context_vars)
            
            
            
            if not self.next_method:
                self.log.info(
                    "Exporting the following env vars:\n%s",
                    "\n".join(f"{k}={v}" for k, v in airflow_context_vars.items()),
                )
            
            self.task.pre_execute(context=context)
            
            self._run_execute_callback(context, self.task)
            
            with set_current_context(context):
                result = self._execute_task(context, task_orig)
            
            self.task.post_execute(context=context, result=result)
        Stats.incr(f"operator_successes_{self.task.task_type}", 1, 1)
        Stats.incr("ti_successes")
    def _run_finished_callback(
        self, callback: TaskStateChangeCallback | None, context: Context, callback_type: str
    ) -> None:
        """Run callback after task finishes"""
        try:
            if callback:
                callback(context)
        except Exception:  
            self.log.exception(f"Error when executing {callback_type} callback")
    def _execute_task(self, context, task_orig):