**Apache Airflow version**:
2.0.0dev

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

**Environment**:
```
./breeze --python=3.8 --backend=postgres --db-reset restart
```

**What happened**:

Sensors in reschedule mode are not rescheduled by scheduler.

**What you expected to happen**:

Sensors in both poke and reschedule mode should work.

**How to reproduce it**:

```
from airflow import DAG
from airflow.sensors.base_sensor_operator import BaseSensorOperator
from airflow.utils.dates import days_ago

class DummySensor(BaseSensorOperator):
    def poke(self, context):
        return False

with DAG(
    "other_dag",
    start_date=days_ago(1),
    schedule_interval="*/5 * * * *",
    catchup=False
) as dag3:
    DummySensor(
        task_id='wait-task',
        poke_interval=60 * 5,
        mode='reschedule'
    )
```

Then:
```
root@053f6ca34e24: /opt/airflow# airflow dags unpause other_dag
Dag: other_dag, paused: False
root@053f6ca34e24: /opt/airflow# airflow scheduler
  ____________       _____________
 ____    |__( )_________  __/__  /________      __
____  /| |_  /__  ___/_  /_ __  /_  __ \_ | /| / /
___  ___ |  / _  /   _  __/ _  / / /_/ /_ |/ |/ /
 _/_/  |_/_/  /_/    /_/    /_/  \____/____/|__/
[2020-12-03 14:18:58,404] {scheduler_job.py:1247} INFO - Starting the scheduler
[2020-12-03 14:18:58,404] {scheduler_job.py:1252} INFO - Processing each file at most -1 times
[2020-12-03 14:18:58,571] {dag_processing.py:250} INFO - Launched DagFileProcessorManager with pid: 63835
[2020-12-03 14:18:58,576] {scheduler_job.py:1757} INFO - Resetting orphaned tasks for active dag runs
[2020-12-03 14:18:58,660] {settings.py:52} INFO - Configured default timezone Timezone('UTC')
[2020-12-03 14:18:58,916] {scheduler_job.py:944} INFO - 1 tasks up for execution:
	<TaskInstance: other_dag.wait-task 2020-12-03 14:10:00+00:00 [scheduled]>
[2020-12-03 14:18:58,920] {scheduler_job.py:973} INFO - Figuring out tasks to run in Pool(name=default_pool) with 128 open slots and 1 task instances ready to be queued
[2020-12-03 14:18:58,921] {scheduler_job.py:1001} INFO - DAG other_dag has 0/16 running and queued tasks
[2020-12-03 14:18:58,921] {scheduler_job.py:1066} INFO - Setting the following tasks to queued state:
	<TaskInstance: other_dag.wait-task 2020-12-03 14:10:00+00:00 [scheduled]>
[2020-12-03 14:18:58,925] {scheduler_job.py:1108} INFO - Sending TaskInstanceKey(dag_id='other_dag', task_id='wait-task', execution_date=datetime.datetime(2020, 12, 3, 14, 10, tzinfo=Timezone('UTC')), try_number=1) to executor with priority 1 and queue default
[2020-12-03 14:18:58,926] {base_executor.py:79} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'other_dag', 'wait-task', '2020-12-03T14:10:00+00:00', '--local', '--pool', 'default_pool', '--subdir', '/files/dags/the_old_issue.py']
[2020-12-03 14:18:58,935] {local_executor.py:80} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'other_dag', 'wait-task', '2020-12-03T14:10:00+00:00', '--local', '--pool', 'default_pool', '--subdir', '/files/dags/the_old_issue.py']
[2020-12-03 14:18:59,063] {dagbag.py:440} INFO - Filling up the DagBag from /files/dags/the_old_issue.py
Running <TaskInstance: other_dag.wait-task 2020-12-03T14:10:00+00:00 [queued]> on host 053f6ca34e24
[2020-12-03 14:19:00,022] {scheduler_job.py:944} INFO - 1 tasks up for execution:
	<TaskInstance: other_dag.wait-task 2020-12-03 14:10:00+00:00 [scheduled]>
[2020-12-03 14:19:00,029] {scheduler_job.py:973} INFO - Figuring out tasks to run in Pool(name=default_pool) with 128 open slots and 1 task instances ready to be queued
[2020-12-03 14:19:00,029] {scheduler_job.py:1001} INFO - DAG other_dag has 0/16 running and queued tasks
[2020-12-03 14:19:00,029] {scheduler_job.py:1066} INFO - Setting the following tasks to queued state:
	<TaskInstance: other_dag.wait-task 2020-12-03 14:10:00+00:00 [scheduled]>
[2020-12-03 14:19:00,033] {scheduler_job.py:1108} INFO - Sending TaskInstanceKey(dag_id='other_dag', task_id='wait-task', execution_date=datetime.datetime(2020, 12, 3, 14, 10, tzinfo=Timezone('UTC')), try_number=1) to executor with priority 1 and queue default
[2020-12-03 14:19:00,033] {base_executor.py:82} ERROR - could not queue task TaskInstanceKey(dag_id='other_dag', task_id='wait-task', execution_date=datetime.datetime(2020, 12, 3, 14, 10, tzinfo=Timezone('UTC')), try_number=1)
[2020-12-03 14:19:00,038] {scheduler_job.py:1199} INFO - Executor reports execution of other_dag.wait-task execution_date=2020-12-03 14:10:00+00:00 exited with status success for try_number 1
[2020-12-03 14:19:00,045] {scheduler_job.py:1235} ERROR - Executor reports task instance <TaskInstance: other_dag.wait-task 2020-12-03 14:10:00+00:00 [queued]> finished (success) although the task says its queued. (Info: None) Was the task killed externally?
[2020-12-03 14:19:01,173] {dagrun.py:429} ERROR - Marking run <DagRun other_dag @ 2020-12-03 14:10:00+00:00: scheduled__2020-12-03T14:10:00+00:00, externally triggered: False> failed
```


**Anything else we need to know**:

Discovered when working on #10790 

Thank @nathadfield for helping discover this issue!

airflow/models/baseoperator.py
downstream tasks have higher weight and will be scheduled more
        aggressively when using positive weight values. This is useful when you
        have multiple dag run instances and prefer to have each dag complete
        before starting upstream tasks of other dags.  When set to
        ``absolute``, the effective weight is the exact ``priority_weight``
        specified without additional weighting. You may want to do this when
        you know exactly what priority weight each task should have.
        Additionally, when set to ``absolute``, there is bonus effect of
        significantly speeding up the task creation process as for very large
        DAGS. Options can be set as string or using the constants defined in
        the static class ``airflow.utils.WeightRule``
    :type weight_rule: str
    :param queue: which queue to target when running this job. Not
        all executors implement queue management, the CeleryExecutor
        does support targeting specific queues.
    :type queue: str
    :param pool: the slot pool this task should run in, slot pools are a
        way to limit concurrency for certain tasks
    :type pool: str
    :param pool_slots: the number of pool slots this task should use (>= 1)
        Values less than 1 are not allowed.
    :type pool_slots: int
    :param sla: time by which the job is expected to succeed. Note that
        this represents the ``timedelta`` after the period is closed. For
        example if you set an SLA of 1 hour, the scheduler would send an email
        soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance
        has not succeeded yet.
        The scheduler pays special attention for jobs with an SLA and
        sends alert
        emails for sla misses. SLA misses are also recorded in the database