Fix doc typos. (#47037)

## Why are these changes needed?
Fix doc typos.

## Related issue number

<!-- For example: "Closes #1234" -->

## Checks

- [x] I've signed off every commit(by using the -s flag, i.e., `git
commit -s`) in this PR.
- [ ] I've run `scripts/format.sh` to lint the changes in this PR.
- [ ] I've included any doc changes needed for
https://docs.ray.io/en/master/.
- [ ] I've added any new APIs to the API Reference. For example, if I
added a
method in Tune, I've added it in `doc/source/tune/api/` under the
           corresponding `.rst` file.
- [x] I've made sure the tests are passing. Note that there might be a
few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
   - [ ] Unit tests
   - [ ] Release tests
   - [x] This PR is not tested :(

Signed-off-by: zhanluxianshen <[email protected]>

doc/source/cluster/running-applications/job-submission/quickstart.rst

@@ -76,7 +76,7 @@ To tell the Ray Jobs CLI how to find your Ray Cluster, pass the Ray Dashboard ad
 
 Alternatively, you can also pass the ``--address=http://127.0.0.1:8265`` flag explicitly to each Ray Jobs CLI command, or prepend each command with ``RAY_ADDRESS=http://127.0.0.1:8265``.
 
-Additionally, if you wish to pass headers per HTTP request to the Cluster, use the `RAY_JOB_HEADERS` environment variable.` This environment variable must be in JSON form.
+Additionally, if you wish to pass headers per HTTP request to the Cluster, use the `RAY_JOB_HEADERS` environment variable. This environment variable must be in JSON form.
 
 .. code-block:: bash
     

doc/source/data/overview.rst

@@ -82,7 +82,7 @@ How does Ray Data compare to other solutions for offline inference?
 
     Ray Data doesn't have a SQL interface and isn't meant as a replacement for generic ETL pipelines like Spark.
 
-    For a more detailed performance comarison between Ray Data and Apache Spark, see `Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker <https://www.anyscale.com/blog/offline-batch-inference-comparing-ray-apache-spark-and-sagemaker>`_.
+    For a more detailed performance comparison between Ray Data and Apache Spark, see `Offline Batch Inference: Comparing Ray, Apache Spark, and SageMaker <https://www.anyscale.com/blog/offline-batch-inference-comparing-ray-apache-spark-and-sagemaker>`_.
 
 Batch inference case studies
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~