The dynamic Sequential Mobile Manipulation Planning (SMMP) framework is essential for the safe and robust operation of mobile manipulators in dynamic environments. Previous research has primarily focused on either motion-level or task level dynamic planning, with limitations in handling state changes that have long-term effects or in generating responsive motions for diverse tasks, respectively. This paper presents a holistic dynamic planning framework that extends the Virtual Kinematic Chain (VKC)-based SMMP method, automating dynamic longterm task planning and reactive whole-body motion generation for SMMP problems. The framework consists of an online task planning module designed to respond to environment changes with long-term effects, a VKC-based whole-body motion planning module for manipulating both rigid and articulated objects, alongside a reactive Model Predictive Control (MPC) module for obstacle avoidance during execution. Simulations and real-world experiments validate the framework, demonstrating its efficacy and validity across sequential mobile manipulation tasks, even in scenarios involving human interference.