**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/ti_deps/deps/test_trigger_rule_dep.py
"""
        All-done trigger rule failure
        """
        ti = self._get_task_instance(TriggerRule.ALL_DONE,
                                     upstream_task_ids=["FakeTaskID",
                                                        "OtherFakeTaskID"])
        dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
            ti=ti,
            successes=1,
            skipped=0,
            failed=0,
            upstream_failed=0,
            done=1,
            flag_upstream_failed=False,
            session="Fake Session"))
        self.assertEqual(len(dep_statuses), 1)
        self.assertFalse(dep_statuses[0].passed)
    def test_none_skipped_tr_success(self):