Recent research has highlighted the potential of ferroelectricity in van der Waals bilayers in providing an unconventional route for improving device performance. Understanding the static and dynamic properties of domain wall (DW) is critical unlocking this potential, as key parameters such as switching field and speed heavily rely on them. In this article, we conduct a theoretical exploration of the fundamental properties of textures in stacking-engineered ferroelectrics using a machine-learning potential model. Our results demonstrate that competition between the switching barrier of stable ferroelectric states and in-plane lattice distortion leads to a DW width of ten nanometers. We also demonstrate that DW motion can drastically reduce the critical ferroelectric switching field of a monodomain by two orders of magnitude and enable domain switching on a picosecond timescale, suggesting the potential for ultrafast and energy-saving non-volatile memory devices. Moreover, twisting the bilayer into a stacking Moiré structure results in a super-paraelectric state, because the ferroelectric order is reversibly broken by DW motion already at ultralow electric fields. These findings offer valuable insights into the behavior and properties of stacking-engineered ferroelectrics, with significant implications for the development of next-generation electronic devices.