**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
dag_id = 'SchedulerJobTest.test_find_executable_task_instances_none'
        task_id_1 = 'dummy'
        dag = DAG(dag_id=dag_id, start_date=DEFAULT_DATE, concurrency=16)
        DummyOperator(dag=dag, task_id=task_id_1)
        dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag))
        scheduler = SchedulerJob()
        session = settings.Session()
        dag_model = DagModel(
            dag_id=dag_id,
            is_paused=False,
            concurrency=dag.concurrency,
            has_task_concurrency_limits=False,
        )
        session.add(dag_model)
        dag.create_dagrun(