input: Write a python function to create, train, and evaluate an SVM model using polynomial kernel with given data.
  assert isinstance(create_and_evaluate_svm_model(data), dict)
  assert 'confusion_matrix' in create_and_evaluate_svm_model(data).keys()
  assert 'classification_report' in create_and_evaluate_svm_model(data).keys()
output: def create_and_evaluate_svm_model(data):
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.svm import SVC
      from sklearn.metrics import confusion_matrix, classification_report
      
      X = data.drop('target', axis=1)
      y = data['target']
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      model = SVC(kernel='poly', degree=3, C=1, random_state=42)
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      
      conf_matrix = confusion_matrix(y_test, y_pred)
      class_report = classification_report(y_test, y_pred, output_dict=True)
      
      return {
          'confusion_matrix': conf_matrix,
          'classification_report': class_report
      }