**Apache Airflow version**:
2.0.0

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):
1.15
**Environment**:

- **Cloud provider or hardware configuration**: 
GKE
- **OS** (e.g. from /etc/os-release):
Ubuntu 18.04

**What happened**:
I just migrated from 1.10.14 to 2.0.0. When I turn on some random dags, the scheduler crashes with the following error:

```python
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1275, in _execute
    self._run_scheduler_loop()
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1377, in _run_scheduler_loop
    num_queued_tis = self._do_scheduling(session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1533, in _do_scheduling
    num_queued_tis = self._critical_section_execute_task_instances(session=session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1132, in _critical_section_execute_task_instances
    queued_tis = self._executable_task_instances_to_queued(max_tis, session=session)
  File "/usr/local/lib/python3.6/dist-packages/airflow/utils/session.py", line 62, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/airflow/jobs/scheduler_job.py", line 1034, in _executable_task_instances_to_queued
    if task_instance.pool_slots > open_slots:
TypeError: '>' not supported between instances of 'NoneType' and 'int'
```
**What you expected to happen**:

I expected those dags would have their tasks scheduled without problems.

**How to reproduce it**:

Can't reproduce it yet. Still trying to figure out if this happens only with specific dags or not.

**Anything else we need to know**:

I couldn't find in which context `task_instance.pool_slots` could be None

airflow/models/taskinstance.py
or_(
                            and_(TR.try_number == try_number, TR.task_id.in_(task_ids))
                            for try_number, task_ids in task_tries.items()
                        ),
                    )
                    for execution_date, task_tries in dates.items()
                ),
            )
            for dag_id, dates in task_id_by_key.items()
        )
        delete_qry = TR.__table__.delete().where(conditions)
        session.execute(delete_qry)
    if job_ids:
        from airflow.jobs.base_job import BaseJob
        for job in session.query(BaseJob).filter(BaseJob.id.in_(job_ids)).all():  
            job.state = State.SHUTDOWN
    if activate_dag_runs and tis:
        from airflow.models.dagrun import DagRun  
        drs = (
            session.query(DagRun)
            .filter(
                DagRun.dag_id.in_({ti.dag_id for ti in tis}),
                DagRun.execution_date.in_({ti.execution_date for ti in tis}),
            )
            .all()
        )
        for dr in drs:
            dr.state = State.RUNNING
            dr.start_date = timezone.utcnow()
class TaskInstanceKey(NamedTuple):