pyarrow.json.read_json ignores nullable=True on fields with non-nullable subfields in explicit_schema parse_options

**Summary:**

The parser seems to be ignoring the "nullable" flag on the parent field.

This behavior may be related to ARROW-16603, but that issue covers the opposite case - failing to include the "not null" constraints in the schema of the output table.

**Reproducible example:**
```java

import pyarrow as pa

interior_struct = pa.struct([
    pa.field(name='some_information', type=pa.int64(), nullable=False)
])

sample_schema = pa.schema([
    pa.field(name='my_struct', type=interior_struct, nullable=True)
])

# The issue persists regardless of the input JSON,
# so long as there is no field named "my_struct" in the input.
# Using an empty JSON object for example
sample_json_bytes = io.BytesIO('{}'.encode())

table = pa.json.read_json(
    input_file=sample_json_bytes,
    parse_options=pa.json.ParseOptions(explicit_schema=sample_schema)
)
print(table)
```
**Expected output:**

Table containing one column:
- Name is "my_struct"
- Type is "interior_struct"
- Contains a single value, which is null

**Actual output:**
```java

ArrowInvalid: JSON parse error: a required field was null
```

**Reporter**: [Ryan Weisman](https://issues.apache.org/jira/browse/ARROW-17835)

_{**Note**: *This issue was originally created as [ARROW-17835](https://issues.apache.org/jira/browse/ARROW-17835). Please see the [migration documentation](https://github.com/apache/arrow/issues/14542) for further details.*}

Start by reproducing the issue through pyarrow.json.read_json with ParseOptions(explicit_schema=sample_schema) and an empty JSON object. Trace how the explicit schema is handled when the parent field is nullable but its nested field is non-nullable, then verify that the result contains a null my_struct value without a required-field error.

Written by the indexing model from the issue text.