Autonomous aerial vehicles, such as NASA's Ingenuity, enable rapid planetary surface exploration beyond the reach of ground-based robots. Thus, NASA is studying a Mars Science Helicopter (MSH), an advanced concept capable of performing long-range science missions and autonomously navigating challenging Martian terrain. Given significant Earth-Mars communication delays and mission complexity, an advanced autonomy framework is required to ensure safe and efficient operation by continuously adapting behavior based on mission objectives and real-time conditions, without human intervention. This study presents a deterministic high-level control framework for aerial exploration, integrating a Finite State Machine (FSM) with Behavior Trees (BTs) to achieve a scalable, robust, and computationally efficient autonomy solution for critical scenarios like deep space exploration. In this paper we outline key capabilities of a possible MSH and detail the FSM-BT hybrid autonomy framework which orchestrates them to achieve the desired objectives. Monte Carlo simulations and real field tests validate the framework, demonstrating its robustness and adaptability to both discrete events and real-time system feedback. These inputs trigger state transitions or dynamically adjust behavior execution, enabling reactive and context-aware responses. The framework is middleware-agnostic, supporting integration with systems like F-Prime and extending beyond aerial robotics.