**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
"""
        this test tests the helper function '_get_states_count_upstream_ti' as a unit and inside update_state
        """
        from airflow.ti_deps.dep_context import DepContext
        get_states_count_upstream_ti = TriggerRuleDep._get_states_count_upstream_ti
        session = settings.Session()
        now = timezone.utcnow()
        dag = DAG(
            'test_dagrun_with_pre_tis',
            start_date=DEFAULT_DATE,
            default_args={'owner': 'owner1'})
        with dag:
            op1 = DummyOperator(task_id='A')
            op2 = DummyOperator(task_id='B')
            op3 = DummyOperator(task_id='C')
            op4 = DummyOperator(task_id='D')
            op5 = DummyOperator(task_id='E', trigger_rule=TriggerRule.ONE_FAILED)
            op1.set_downstream([op2, op3])  
            op4.set_upstream([op3, op2])  
            op5.set_upstream([op2, op3, op4])