**Apache Airflow version**: 2.0.0a1

**What happened**:

On my dev environment I have a lot of DAG runs with failed tasks, where the *downstream* task instances then have been set to `upstream_failed`. The scheduler is now refusing to schedule more instances for such DAGs.

The scheduler looks for such task instances that are not in the `State.finished` set:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/jobs/scheduler_job.py#L1482-L1488

However, `State.finished` is currently not a proper inverse of *running* states, it only includes success, failed, and skipped:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/utils/state.py#L110-L114

What is missing is the task instance `upstream_failed` state, so currently my log is being filled (very rapidly) with:

```
[2020-10-18 13:22:18,503] {{scheduler_job.py:1658}} INFO - DAG test_dag already has 26 active runs, not queuing any more tasks
```

Those 26 'active runs' are simply dag runs that failed and have 'upstream_failed' task instances.

Either `upstream_failed` should be added to `State.finished`, or the Scheduler task instance test should explicitly take that state into account.
airflow/models/dagrun.py
return (
            '<DagRun {dag_id} @ {execution_date}: {run_id}, '
            'externally triggered: {external_trigger}>'
        ).format(
            dag_id=self.dag_id,
            execution_date=self.execution_date,
            run_id=self.run_id,
            external_trigger=self.external_trigger)
    def get_state(self):
        return self._state
    def set_state(self, state):
        if self._state != state:
            self._state = state
            self.end_date = timezone.utcnow() if self._state in State.finished else None
    @declared_attr
    def state(self):