[SPARK-52339][SQL] Fix comparison of InMemoryFileIndex instances

[bersprockets]

What changes were proposed in this pull request?

This PR changes InMemoryFileIndex#equals to compare a non-distinct collection of root paths rather than a distinct set of root paths. Without this change, InMemoryFileIndex#equals considers the following two collections of root paths to be equal, even though they represent a different number of rows:

["/tmp/test", "/tmp/test"]
["/tmp/test", "/tmp/test", "/tmp/test"]

Why are the changes needed?

The bug can cause correctness issues, e.g.

// create test data
val data = Seq((1, 2), (2, 3)).toDF("a", "b")
data.write.mode("overwrite").csv("/tmp/test")

val fileList1 = List.fill(2)("/tmp/test")
val fileList2 = List.fill(3)("/tmp/test")

val df1 = spark.read.schema("a int, b int").csv(fileList1: _*)
val df2 = spark.read.schema("a int, b int").csv(fileList2: _*)

df1.count() // correctly returns 4
df2.count() // correctly returns 6

// the following is the same as above, except df1 is persisted
val df1 = spark.read.schema("a int, b int").csv(fileList1: _*).persist
val df2 = spark.read.schema("a int, b int").csv(fileList2: _*)

df1.count() // correctly returns 4
df2.count() // incorrectly returns 4!!

In the above example, df1 and df2 were created with a different number of paths: df1 has 2, and df2 has 3. But since the distinct set of root paths is the same (e.g., Set("/tmp/test") == Set("/tmp/test")), the two dataframes are considered equal. Thus, when df1 is persisted, df2 uses df1's cached plan.

The same bug also causes inappropriate exchange reuse.

Does this PR introduce any user-facing change?

No.

How was this patch tested?

New unit test.

Was this patch authored or co-authored using generative AI tooling?

No.

[HyukjinKwon]

looks making sense to me