**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
"""
        Calculates the previous schedule for this dag in UTC
        :param dttm: utc datetime
        :return: utc datetime
        """
        if isinstance(self.normalized_schedule_interval, str):
            
            
            dttm = pendulum.instance(dttm)
            naive = timezone.make_naive(dttm, self.timezone)
            cron = croniter(self.normalized_schedule_interval, naive)
            
            if not self.is_fixed_time_schedule():
                
                delta = naive - cron.get_prev(datetime)
                previous = dttm.in_timezone(self.timezone) - delta
            else:
                
                naive = cron.get_prev(datetime)
                tz = pendulum.timezone(self.timezone.name)
                previous = timezone.make_aware(naive, tz)
            return timezone.convert_to_utc(previous)
        elif self.normalized_schedule_interval is not None:
            return timezone.convert_to_utc(dttm - self.normalized_schedule_interval)
    def next_dagrun_info(