To solve the optimization problem described, we need to formulate it as a linear programming problem. The goal is to maximize profit given the constraints on crafting and polishing hours.

Let's denote:
- $x_1$ as the number of bedside tables produced,
- $x_2$ as the number of bookcases produced.

The objective function (profit) can be represented as $200x_1 + 500x_2$, since each bedside table contributes $200 to profit and each bookcase contributes $500.

Constraints:
1. Crafting hours: $2.5x_1 + 5x_2 \leq 30$ (since each bedside table requires 2.5 crafting hours and each bookcase requires 5 crafting hours, and there are a maximum of 30 crafting hours available).
2. Polishing hours: $1.5x_1 + 3x_2 \leq 20$ (since each bedside table requires 1.5 polishing hours and each bookcase requires 3 polishing hours, and there are a maximum of 20 polishing hours available).
3. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$, since the number of products cannot be negative.

Now, let's translate this problem into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Furniture_Factory")

# Define variables
x1 = m.addVar(name='bedside_tables', vtype=GRB.CONTINUOUS, lb=0)
x2 = m.addVar(name='bookcases', vtype=GRB.CONTINUOUS, lb=0)

# Set the objective function
m.setObjective(200*x1 + 500*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2.5*x1 + 5*x2 <= 30, name='crafting_hours')
m.addConstr(1.5*x1 + 3*x2 <= 20, name='polishing_hours')

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Bedside tables: {x1.x}")
    print(f"Bookcases: {x2.x}")
    print(f"Maximum profit: ${200*x1.x + 500*x2.x:.2f}")
else:
    print("No optimal solution found")
```