To solve this linear programming optimization problem, we first need to define the variables and constraints based on the given information.

Let's denote:
- \(x_1\) as the number of vase décors produced.
- \(x_2\) as the number of wood canvases produced.

The objective function is to maximize profit. Given that each vase décor yields a profit of $50 and each wood canvas yields a profit of $85, the objective function can be written as:
\[ \text{Maximize:} \quad 50x_1 + 85x_2 \]

Now, let's define the constraints based on the time available for carving and polishing:
- Carving constraint: \(20x_1 + 18x_2 \leq 400\), since each vase décor requires 20 minutes of carving and each wood canvas requires 18 minutes of carving, with a total of 400 minutes available.
- Polishing constraint: \(14x_1 + 8x_2 \leq 640\), as each vase décor requires 14 minutes of polishing and each wood canvas requires 8 minutes of polishing, with a total of 640 minutes available.

Additionally, since the number of décors cannot be negative:
- \(x_1 \geq 0\) and \(x_2 \geq 0\).

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

```python
from gurobipy import *

# Create a new model
model = Model("Wood_Decor_Problem")

# Define the variables
x1 = model.addVar(lb=0, vtype=GRB.INTEGER, name="vase_decors")
x2 = model.addVar(lb=0, vtype=GRB.INTEGER, name="wood_canvases")

# Define the objective function
model.setObjective(50*x1 + 85*x2, GRB.MAXIMIZE)

# Define the constraints
model.addConstr(20*x1 + 18*x2 <= 400, "carving_time")
model.addConstr(14*x1 + 8*x2 <= 640, "polishing_time")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of vase décors: {x1.x}")
    print(f"Number of wood canvases: {x2.x}")
    print(f"Maximum profit: ${50*x1.x + 85*x2.x}")
else:
    print("No optimal solution found")
```