**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.
tests/jobs/test_scheduler_job.py
"""Make sure a task in an orphaned state is not reset if it has no dagrun. """
        dag_id = 'test_reset_orphaned_tasks_nonexistent_dagrun'
        dag = DAG(dag_id=dag_id, start_date=DEFAULT_DATE, schedule_interval='@daily')
        task_id = dag_id + '_task'
        task = DummyOperator(task_id=task_id, dag=dag)
        scheduler = SchedulerJob()
        session = settings.Session()
        ti = TaskInstance(task=task, execution_date=DEFAULT_DATE)
        session.add(ti)
        session.flush()
        ti.refresh_from_db()
        ti.state = State.SCHEDULED
        session.merge(ti)
        session.flush()
        self.assertEqual(0, scheduler.adopt_or_reset_orphaned_tasks(session=session))
        session.rollback()
    def test_reset_orphaned_tasks_no_orphans(self):