diff --git a/cpp/src/arrow/dataset/partition.cc b/cpp/src/arrow/dataset/partition.cc
index 4631b4f02e5..dcb91fa8fac 100644
--- a/cpp/src/arrow/dataset/partition.cc
+++ b/cpp/src/arrow/dataset/partition.cc
@@ -306,6 +306,16 @@ class KeyValuePartitioningInspectImpl {
     return ::arrow::schema(std::move(fields));
   }
 
+  std::vector<std::string> FieldNames() {
+    std::vector<std::string> names;
+    names.reserve(name_to_index_.size());
+
+    for (auto kv : name_to_index_) {
+      names.push_back(kv.first);
+    }
+    return names;
+  }
+
  private:
   std::unordered_map<std::string, int> name_to_index_;
   std::vector<std::set<std::string>> values_;
@@ -646,15 +656,29 @@ class HivePartitioningFactory : public PartitioningFactory {
       }
     }
 
+    field_names_ = impl.FieldNames();
     return impl.Finish(&dictionaries_);
   }
 
   Result<std::shared_ptr<Partitioning>> Finish(
       const std::shared_ptr<Schema>& schema) const override {
-    return std::shared_ptr<Partitioning>(new HivePartitioning(schema, dictionaries_));
+    if (dictionaries_.empty()) {
+      return std::make_shared<HivePartitioning>(schema, dictionaries_);
+    } else {
+      for (FieldRef ref : field_names_) {
+        // ensure all of field_names_ are present in schema
+        RETURN_NOT_OK(ref.FindOne(*schema).status());
+      }
+
+      // drop fields which aren't in field_names_
+      auto out_schema = SchemaFromColumnNames(schema, field_names_);
+
+      return std::make_shared<HivePartitioning>(std::move(out_schema), dictionaries_);
+    }
   }
 
  private:
+  std::vector<std::string> field_names_;
   ArrayVector dictionaries_;
   PartitioningFactoryOptions options_;
 };
diff --git a/python/pyarrow/tests/test_dataset.py b/python/pyarrow/tests/test_dataset.py
index e71bb7cee40..b7200ed497c 100644
--- a/python/pyarrow/tests/test_dataset.py
+++ b/python/pyarrow/tests/test_dataset.py
@@ -1303,6 +1303,35 @@ def test_open_dataset_non_existing_file():
         ds.dataset('file:i-am-not-existing.parquet', format='parquet')
 
 
+@pytest.mark.parquet
+@pytest.mark.parametrize('partitioning', ["directory", "hive"])
+def test_open_dataset_partitioned_dictionary_type(tempdir, partitioning):
+    # ARROW-9288
+    import pyarrow.parquet as pq
+    table = pa.table({'a': range(9), 'b': [0.] * 4 + [1.] * 5})
+
+    path = tempdir / "dataset"
+    path.mkdir()
+
+    for part in ["A", "B", "C"]:
+        fmt = "{}" if partitioning == "directory" else "part={}"
+        part = path / fmt.format(part)
+        part.mkdir()
+        pq.write_table(table, part / "test.parquet")
+
+    if partitioning == "directory":
+        part = ds.DirectoryPartitioning.discover(
+            ["part"], max_partition_dictionary_size=-1)
+    else:
+        part = ds.HivePartitioning.discover(max_partition_dictionary_size=-1)
+
+    dataset = ds.dataset(str(path), partitioning=part)
+    expected_schema = table.schema.append(
+        pa.field("part", pa.dictionary(pa.int32(), pa.string()))
+    )
+    assert dataset.schema.equals(expected_schema)
+
+
 @pytest.fixture
 def s3_example_simple(s3_connection, s3_server):
     from pyarrow.fs import FileSystem