Recent LLM-based embodied agents have advanced in high-level task planning. Yet, they frequently suffer from hallucinations and brittle reasoning due to passive observation—either omitting fine-grained attributes or overwhelming the planner with irrelevant information. We propose SceneQueryPlanner, a memory-environment dual-grounded agent that integrates active information seeking into the planning loop by decomposing instructions and selectively executing environmental interactions or structured queries over a dynamic 3D scene graph to retrieve task-relevant information on demand. By treating information acquisition as a first-class planning action, our approach enables explicit verification and supports reliable long-horizon reasoning over object attributes. For rigorous evaluation, we introduce AttPlan-Bench, a benchmark of multi-room, long-horizon household tasks requiring attribute-aware reasoning. Experiments demonstrate that our dual-grounding strategy outperforms passive baselines, yielding more stable and scalable reasoning across diverse tasks.