**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
session.query(TaskInstance).delete()
        session.commit()
        ti.state = State.RUNNING
        session.merge(ti)
        session.commit()
        scheduler_job._change_state_for_tasks_failed_to_execute()  
        ti.refresh_from_db()
        self.assertEqual(State.RUNNING, ti.state)
    def test_adopt_or_reset_orphaned_tasks(self):
        session = settings.Session()
        dag = DAG(
            'test_execute_helper_reset_orphaned_tasks',
            start_date=DEFAULT_DATE,
            default_args={'owner': 'owner1'})
        with dag:
            op1 = DummyOperator(task_id='op1')
        dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag))
        dag.clear()
        dr = dag.create_dagrun(run_type=DagRunType.SCHEDULED,
                               state=State.RUNNING,
                               execution_date=DEFAULT_DATE,
                               start_date=DEFAULT_DATE,
                               session=session)
        dr2 = dag.create_dagrun(run_type=DagRunType.BACKFILL_JOB,
                                state=State.RUNNING,
                                execution_date=DEFAULT_DATE + datetime.timedelta(1),
                                start_date=DEFAULT_DATE,
                                session=session)
        ti = dr.get_task_instance(task_id=op1.task_id, session=session)
        ti.state = State.SCHEDULED