Fix unscheduled tasks left on a stale Dag version

[FrankYang0529]

Why

• Before Fix memory issue: remove eager loading of all TIs in scheduler #60956, a task instance with state = None can be updated to the latest Dag version when the Dag changed. The code was a Python loop over the run's task instances and None in State.unfinished is True.
• Fix memory issue: remove eager loading of all TIs in scheduler #60956 rewrote that loop as a bulk UPDATE ... WHERE state IN (...) so the scheduler no longer loads every task instance into memory. State.unfinished still contains None, but SQL IN doesn't match NULL. The scheduler cannot update the task instances to the latest Dag version that have not been scheduled yet.

How

• Use the same guard in other call sites like taskmap, taskinstance: or_(TI.state.is_(None), TI.state.in_(State.unfinished)).

Verification

• uv run --project airflow-core pytest airflow-core/tests/unit/jobs/test_scheduler_job.py -q

---

Was generative AI tooling used to co-author this PR?

• Yes - Claude Code

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.