### 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)

ForeignKeyConstraint(
            [trigger_id],
            ["trigger.id"],
            name="task_instance_trigger_id_fkey",
            ondelete="CASCADE",
        ),
        ForeignKeyConstraint(
            [dag_id, run_id],
            ["dag_run.dag_id", "dag_run.run_id"],
            name="task_instance_dag_run_fkey",
            ondelete="CASCADE",
        ),
    )
    dag_model = relationship(
        "DagModel",
        primaryjoin="TaskInstance.dag_id == DagModel.dag_id",
        foreign_keys=dag_id,
        uselist=False,
        innerjoin=True,
        viewonly=True,
    )
    trigger = relationship("Trigger", uselist=False)
    triggerer_job = association_proxy("trigger", "triggerer_job")
    dag_run = relationship("DagRun", back_populates="task_instances", lazy="joined", innerjoin=True)
    rendered_task_instance_fields = relationship("RenderedTaskInstanceFields", lazy="noload", uselist=False)
    execution_date = association_proxy("dag_run", "execution_date")
    task_instance_note = relationship("TaskInstanceNote", back_populates="task_instance", uselist=False)
    note = association_proxy("task_instance_note", "content", creator=_creator_note)
    task: Operator  
    def __init__(