**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
if task_end_dates and next_run_date:
            min_task_end_date = min(task_end_dates)
            if next_run_date > min_task_end_date:
                return None
        return next_run_date
    def get_run_dates(self, start_date, end_date=None):
        """
        Returns a list of dates between the interval received as parameter using this
        dag's schedule interval. Returned dates can be used for execution dates.
        :param start_date: the start date of the interval
        :type start_date: datetime
        :param end_date: the end date of the interval, defaults to timezone.utcnow()
        :type end_date: datetime
        :return: a list of dates within the interval following the dag's schedule
        :rtype: list
        """
        run_dates = []
        using_start_date = start_date
        using_end_date = end_date
        
        using_start_date = using_start_date or min(t.start_date for t in self.tasks)
        using_end_date = using_end_date or timezone.utcnow()
        
        
        next_run_date = self.normalize_schedule(using_start_date) if not self.is_subdag else using_start_date
        while next_run_date and next_run_date <= using_end_date:
            run_dates.append(next_run_date)
            next_run_date = self.following_schedule(next_run_date)
        return run_dates
    def normalize_schedule(self, dttm):