Large Language Models (LLMs) have enabled embodied agents to perform high-level planning, yet grounding language-based plans into the physical world remains challenging. While feedback-based closed-loop planning is the dominant paradigm, existing methods typically rely on a single scalar feedback signal, which conflates physically infeasible actions that violate hard constraints with strategically sub-optimal but valid choices. We identify this structural limitation as Signal Confounding, which leads to ambiguous feedback and unreliable plan correction. To address this issue, we propose Decoupled Dual-Critic Guidance (DDCG), a framework that explicitly disentangles feedback into two complementary signals: a Feasibility Critic (C_F) that enforces physical and logical executability constraints, and a Quality Critic (C_Q) that evaluates the strategic value of feasible actions. This decoupled guidance enables precise error attribution during planning without requiring parameter updates to the LLM planner. We provide a theoretical interpretation of DDCG as critic-regularized planning with hard feasibility constraints. Experiments on two embodied planning benchmarks, VirtualHome and ScienceWorld, show that DDCG consistently improves plan executability and task success, particularly in long-horizon settings, highlighting the importance of structured feedback for reliable LLM-based planning.