**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
run_type=DagRunType.SCHEDULED,
            execution_date=date,
            state=State.RUNNING,
        )
        date = dag.following_schedule(date)
        dr2 = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=date,
            state=State.RUNNING,
        )
        date = dag.following_schedule(date)
        dr3 = dag.create_dagrun(
            run_type=DagRunType.SCHEDULED,
            execution_date=date,
            state=State.RUNNING,
        )
        ti1 = TaskInstance(task1, dr1.execution_date)
        ti2 = TaskInstance(task1, dr2.execution_date)
        ti3 = TaskInstance(task1, dr3.execution_date)
        ti1.state = State.RUNNING
        ti2.state = State.RUNNING
        ti3.state = State.RUNNING
        session.merge(ti1)
        session.merge(ti2)
        session.merge(ti3)
        session.flush()
        res = scheduler._executable_task_instances_to_queued(max_tis=100, session=session)
        self.assertEqual(0, len(res))
        session.rollback()
    def test_enqueue_task_instances_with_queued_state(self):