**Description**

For a DAG that looks like this:

```
A >> B
```

If A fails, B goes into "upstream_failed" state. If a user then marks A "success", B will remain in "upstream_failed" state. It will not automatically start running. This scenario often happens if the failure of A is dealt with outside of Airflow and the user does not want Airflow to run A again. But he usually expect Airflow to run B after A is marked "success".

**Use case / motivation**

After A is marked "success", its downstream tasks B should be cleared automatically and get ready to be scheduled. To avoid changing this behaviour completely, there can be a toggle button next to "Success" that lets the user decide if he wants Airflow to automatically clear downstream tasks that are in "upstream_failed" state.
airflow/models/dag.py
self,
        date_last_automated_dagrun: Optional[pendulum.DateTime],
    ) -> Tuple[Optional[pendulum.DateTime], Optional[pendulum.DateTime]]:
        """
        Get information about the next DagRun of this dag after ``date_last_automated_dagrun`` -- the
        execution date, and the earliest it could be scheduled
        :param date_last_automated_dagrun: The max(execution_date) of existing
            "automated" DagRuns for this dag (scheduled or backfill, but not
            manual)
        """
        if (
            self.schedule_interval == "@once" and date_last_automated_dagrun
        ) or self.schedule_interval is None:
            
            return (None, None)
        next_execution_date = self.next_dagrun_after_date(date_last_automated_dagrun)
        if next_execution_date is None:
            return (None, None)
        if self.schedule_interval == "@once":
            
            return (next_execution_date, next_execution_date)
        return (next_execution_date, self.following_schedule(next_execution_date))
    def next_dagrun_after_date(self, date_last_automated_dagrun: Optional[pendulum.DateTime]):