**Apache Airflow version**: 2.0.0
**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):
**Environment**:

- **OS** (e.g. from /etc/os-release): Debian GNU/Linux 10 (buster)
- **Kernel** (e.g. `uname -a`): Linux 6ae65b86e112 5.4.0-52-generic #57-Ubuntu SMP Thu Oct 15 10:57:00 UTC 2020 x86_64 GNU/Linux
- **Others**: Python 3.8

**What happened**:

After migrating one of our development Airflow instances from 1.10.14 to 2.0.0, the scheduler started to refuse to schedule tasks for a DAG that did not actually exceed its `max_active_runs`.

When it did this the following error would be logged:

```
DAG <dag_name> already has 577 active runs, not queuing any tasks for run 2020-12-17 08:05:00+00:00
```

A bit of digging revealed that this DAG had task instances associated with it that are in the `removed` state. As soon as I forced the task instances that are in the `removed` state into the `failed` state, the tasks would be scheduled.

I believe the root cause of the issue is that [this filter](https://github.com/apache/airflow/blob/master/airflow/jobs/scheduler_job.py#L1506) does not filter out tasks that are in the `removed` state.

**What you expected to happen**:

I expected the task instances in the DAG to be scheduled, because the DAG did not actually exceed the number of `max_active_runs`.

**How to reproduce it**:

I think the best approach to reproduce it is as follows:
1. Create a DAG and set `max_active_runs` to 1.
2. Ensure the DAG has ran successfully a number of times, such that it has some history associated with it.
3. Set one historical task instance to the `removed` state (either by directly updating it in the DB, or deleting a task from a DAG before it has been able to execute).

**Anything else we need to know**:

The Airflow instance that I ran into this issue with contains about 3 years of task history, which means that we actually had quite a few task instances that are in the `removed` state, but there is no easy way to delete those from the Web UI.

A work around is to set the tasks to `failed`, which will allow the scheduler to proceed.
airflow/jobs/scheduler_job.py
"""Sends SLA Callbacks to DagFileProcessor if tasks have SLAs set and check_slas=True"""
        if not settings.CHECK_SLAS:
            return
        if not any(isinstance(ti.sla, timedelta) for ti in dag.tasks):
            self.log.debug("Skipping SLA check for %s because no tasks in DAG have SLAs", dag)
            return
        if not self.processor_agent:
            raise ValueError("Processor agent is not started.")
        self.processor_agent.send_sla_callback_request_to_execute(
            full_filepath=dag.fileloc, dag_id=dag.dag_id
        )
    @provide_session
    def _emit_pool_metrics(self, session: Session = None) -> None:
        pools = models.Pool.slots_stats(session=session)
        for pool_name, slot_stats in pools.items():
            Stats.gauge(f'pool.open_slots.{pool_name}', slot_stats["open"])
            Stats.gauge(f'pool.queued_slots.{pool_name}', slot_stats[State.QUEUED])
            Stats.gauge(f'pool.running_slots.{pool_name}', slot_stats[State.RUNNING])
    @provide_session
    def heartbeat_callback(self, session: Session = None) -> None:
        Stats.incr('scheduler_heartbeat', 1, 1)
    @provide_session
    def adopt_or_reset_orphaned_tasks(self, session: Session = None):