[Doc][Improvment] Performance evaluation of filter pushdown

**Is your feature request related to a problem? Please describe.**
Now that GraphAr already support ***property-based*** filter pushdown https://github.com/alibaba/GraphAr/pull/221, we need to evaluate the performance improvement.

**Describe the solution you'd like**
- Test Overview: Use `VertexReader` and `EdgeReader` to load all vertices and edges of [ldbc-sf100](https://graphscope.oss-cn-beijing.aliyuncs.com/graphar/ldbc-sf100.tar.gz) with filter conditions at the same time, and compare the performance when the push-down options are turned on and off.
- Filter conditions:
- Select firstName, lastName and gender columns: `select("firstName", "lastName", "gender");`
- Filter records with female gender: `filter(col(gender) === "female");`
- push-down options:
- `spark.sql.parquet.filterPushdown`
- `spark.sql.optimizer.nestedSchemaPruning.enabled`
- expected result:
- The logs of parquet push down are printed.
- Memory load: reduced;
- CPU load: reduced;
- Execution time: not much change.

**Describe alternatives you've considered**
None.

**Additional context**
Test code snippet:
```scala
object GraphArPushDown {

def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("read GraphAr with(out) filter pushdown")
.config("spark.sql.parquet.filterPushdown", args(0))
.config("spark.sql.optimizer.nestedSchemaPruning.enabled", args(0))
.master("local[*]")
.getOrCreate()

val start = System.nanoTime()
// construct the vertex information
val prefix = args(1)
val vertex_yaml = prefix + "person.vertex.yml"
val vertex_info = VertexInfo.loadVertexInfo(vertex_yaml, spark)
// construct the vertex reader
val vertex_reader = new VertexReader(prefix, vertex_info, spark)

// test reading the number of vertices
assert(vertex_reader.readVerticesNumber() == 448626)
val vertex_pg = vertex_info.getPropertyGroup("id")
var newColumns = (0 to 50000).map(i => {
val columnName = s"gender$i"
val newColumn = concat(
col("firstName"),
lit("-"),
col("lastName"),
lit("-"),
col("gender")
).as(columnName)
newColumn
})
newColumns +:= col("firstName")
newColumns +:= col("lastName")
newColumns +:= col("gender")

val vertex_df = vertex_reader
.readVertexPropertyGroup(vertex_pg, addIndex = false)
.select(newColumns: _*)
.filter(col("gender") === "male")
vertex_df.explain()
assert(vertex_df.count() == 223843)

// construct the edge information
val edge_yaml = prefix + "person_knows_person.yml"
val edge_info = EdgeInfo.loadEdgeInfo(edge_yaml, spark)
// construct the edge reader
var adj_list_type = AdjListType.ordered_by_source
var edge_reader = new EdgeReader(prefix, edge_info, adj_list_type, spark)

assert(edge_reader.readEdgesNumber() == 19941198)
var edge_df = edge_reader.readEdges(false)
assert(edge_df.count() == 19941198)

adj_list_type = AdjListType.ordered_by_dest
edge_reader = new EdgeReader(prefix, edge_info, adj_list_type, spark)

assert(edge_reader.readEdgesNumber() == 19941198)
edge_df = edge_reader.readEdges(false)
assert(edge_df.count() == 19941198)

println(s"execution time: ${(System.nanoTime() - start) / 1e9} s")
}
}

```

Start from the GraphArPushDown example in the issue and run it with the ldbc-sf100 dataset using VertexReader and EdgeReader. Compare both settings of spark.sql.parquet.filterPushdown and spark.sql.optimizer.nestedSchemaPruning.enabled, inspect explain() output and the logs, and record memory, CPU, and execution-time results for the documented filter conditions.

Written by the indexing model from the issue text.