Here's how we can formulate this problem and the corresponding Gurobi code:

**Decision Variables:**

* `x`: Number of bedside tables produced daily.
* `y`: Number of bookcases produced daily.

**Objective Function:**

Maximize profit: `200x + 500y`

**Constraints:**

* Crafting time constraint: `2.5x + 5y <= 30`
* Polishing time constraint: `1.5x + 3y <= 20`
* Non-negativity constraints: `x >= 0`, `y >= 0`

```python
import gurobipy as gp

# Create a new model
m = gp.Model("furniture_production")

# Create decision variables
x = m.addVar(vtype=gp.GRB.CONTINUOUS, name="bedside_tables")
y = m.addVar(vtype=gp.GRB.CONTINUOUS, name="bookcases")

# Set objective function
m.setObjective(200*x + 500*y, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(2.5*x + 5*y <= 30, "crafting_constraint")
m.addConstr(1.5*x + 3*y <= 20, "polishing_constraint")
m.addConstr(x >= 0, "x_nonnegativity")
m.addConstr(y >= 0, "y_nonnegativity")

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal production plan:")
    print(f"Bedside tables: {x.x}")
    print(f"Bookcases: {y.x}")
    print(f"Maximum profit: ${m.objVal}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The problem is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
