## What changes were proposed in this pull request?

Currently,  even if I explicitly disable Hive support in SparkR session as below:

```r
sparkSession <- sparkR.session("local[4]", "SparkR", Sys.getenv("SPARK_HOME"),
                               enableHiveSupport = FALSE)
```

produces when the Hadoop version is not supported by our Hive fork:

```
java.lang.reflect.InvocationTargetException
...
Caused by: java.lang.IllegalArgumentException: Unrecognized Hadoop major version number: 3.1.1.3.1.0.0-78
	at org.apache.hadoop.hive.shims.ShimLoader.getMajorVersion(ShimLoader.java:174)
	at org.apache.hadoop.hive.shims.ShimLoader.loadShims(ShimLoader.java:139)
	at org.apache.hadoop.hive.shims.ShimLoader.getHadoopShims(ShimLoader.java:100)
	at org.apache.hadoop.hive.conf.HiveConf$ConfVars.<clinit>(HiveConf.java:368)
	... 43 more
Error in handleErrors(returnStatus, conn) :
  java.lang.ExceptionInInitializerError
	at org.apache.hadoop.hive.conf.HiveConf.<clinit>(HiveConf.java:105)
	at java.lang.Class.forName0(Native Method)
	at java.lang.Class.forName(Class.java:348)
	at org.apache.spark.util.Utils$.classForName(Utils.scala:193)
	at org.apache.spark.sql.SparkSession$.hiveClassesArePresent(SparkSession.scala:1116)
	at org.apache.spark.sql.api.r.SQLUtils$.getOrCreateSparkSession(SQLUtils.scala:52)
	at org.apache.spark.sql.api.r.SQLUtils.getOrCreateSparkSession(SQLUtils.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```

The root cause is that:

```
SparkSession.hiveClassesArePresent
```

check if the class is loadable or not to check if that's in classpath but `org.apache.hadoop.hive.conf.HiveConf` has a check for Hadoop version as static logic which is executed right away. This throws an `IllegalArgumentException` and that's not caught:

https://github.com/apache/spark/blob/36edbac1c8337a4719f90e4abd58d38738b2e1fb/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala#L1113-L1121

So, currently, if users have a Hive built-in Spark with unsupported Hadoop version by our fork (namely 3+), there's no way to use SparkR even though it could work.

This PR just propose to change the order of bool comparison so that we can don't execute `SparkSession.hiveClassesArePresent` when:

  1. `enableHiveSupport` is explicitly disabled
  2. `spark.sql.catalogImplementation` is `in-memory`

so that we **only** check `SparkSession.hiveClassesArePresent` when Hive support is explicitly enabled by short circuiting.

## How was this patch tested?

It's difficult to write a test since we don't run tests against Hadoop 3 yet. See https://github.com/apache/spark/pull/21588. Manually tested.

Closes #23356 from HyukjinKwon/SPARK-26422.

Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 305e9b5ad22b428501fd42d3730d73d2e09ad4c5)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>