[YARN][DOC] Remove non-Yarn specific configurations from running-on-yarn.md

## What changes were proposed in this pull request?

Remove `spark.driver.memory`, `spark.executor.memory`,  `spark.driver.cores`, and `spark.executor.cores` from `running-on-yarn.md` as they are not Yarn-specific, and they are also defined in`configuration.md`.

## How was this patch tested?
Build passed & Manually check.

Author: Weiqing Yang <yangweiqing001@gmail.com>

Closes #15869 from weiqingy/yarnDoc.

docs/running-on-yarn.md

@@ -117,28 +117,6 @@ To use a custom metrics.properties for the application master and executors, upd
     Use lower-case suffixes, e.g. <code>k</code>, <code>m</code>, <code>g</code>, <code>t</code>, and <code>p</code>, for kibi-, mebi-, gibi-, tebi-, and pebibytes, respectively.
   </td>
 </tr>
-<tr>
-  <td><code>spark.driver.memory</code></td>
-  <td>1g</td>
-  <td>
-    Amount of memory to use for the driver process, i.e. where SparkContext is initialized.
-    (e.g. <code>1g</code>, <code>2g</code>).
-
-    <br /><em>Note:</em> In client mode, this config must not be set through the <code>SparkConf</code>
-    directly in your application, because the driver JVM has already started at that point.
-    Instead, please set this through the <code>--driver-memory</code> command line option
-    or in your default properties file.
-  </td>
-</tr>
-<tr>
-  <td><code>spark.driver.cores</code></td>
-  <td><code>1</code></td>
-  <td>
-    Number of cores used by the driver in YARN cluster mode.
-    Since the driver is run in the same JVM as the YARN Application Master in cluster mode, this also controls the cores used by the YARN Application Master.
-    In client mode, use <code>spark.yarn.am.cores</code> to control the number of cores used by the YARN Application Master instead.
-  </td>
-</tr>
 <tr>
   <td><code>spark.yarn.am.cores</code></td>
   <td><code>1</code></td>
@@ -233,13 +211,6 @@ To use a custom metrics.properties for the application master and executors, upd
     Comma-separated list of jars to be placed in the working directory of each executor.
   </td>
 </tr>
-<tr>
-  <td><code>spark.executor.cores</code></td>
-  <td>1 in YARN mode, all the available cores on the worker in standalone mode.</td>
-  <td>
-    The number of cores to use on each executor. For YARN and standalone mode only.
-  </td>
-</tr>
 <tr>
  <td><code>spark.executor.instances</code></td>
   <td><code>2</code></td>
@@ -247,13 +218,6 @@ To use a custom metrics.properties for the application master and executors, upd
     The number of executors for static allocation. With <code>spark.dynamicAllocation.enabled</code>, the initial set of executors will be at least this large.
   </td>
 </tr>
-<tr>
-  <td><code>spark.executor.memory</code></td>
-  <td>1g</td>
-  <td>
-    Amount of memory to use per executor process (e.g. <code>2g</code>, <code>8g</code>).
-  </td>
-</tr>
 <tr>
  <td><code>spark.yarn.executor.memoryOverhead</code></td>
   <td>executorMemory * 0.10, with minimum of 384 </td>