In a data engineering project, you are working with two DataFrames: 'df_orders' containing columns 'order_id', 'customer_id', and 'order_date', and 'df_customers' with columns 'customer_id', 'customer_name', and 'customer_age'. The project requires analyzing customer orders while ensuring all orders are included in the analysis, even if some customer details are missing. Considering the need for a comprehensive analysis that includes all orders, which of the following Spark SQL join operations would you use to achieve this? Choose the best option that meets the project requirements.