**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 not start_date:
            start_date = (timezone.utcnow() - timedelta(30)).date()
            start_date = timezone.make_aware(datetime.combine(start_date, datetime.min.time()))
        tis = session.query(TaskInstance).filter(
            TaskInstance.dag_id == self.dag_id,
            TaskInstance.execution_date >= start_date,
            TaskInstance.task_id.in_([t.task_id for t in self.tasks]),
        )
        
        if end_date or not self.allow_future_exec_dates:
            end_date = end_date or timezone.utcnow()
            tis = tis.filter(TaskInstance.execution_date <= end_date)
        if state:
            if isinstance(state, str):
                tis = tis.filter(TaskInstance.state == state)
            else:
                
                if None in state:
                    if all(x is None for x in state):
                        tis = tis.filter(TaskInstance.state.is_(None))
                    else:
                        not_none_state = [s for s in state if s]
                        tis = tis.filter(
                            or_(TaskInstance.state.in_(not_none_state), TaskInstance.state.is_(None))