To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of vase décors produced,
- $x_2$ as the number of wood canvases produced.

The objective function, which represents the total profit, can be written as:
\[ 50x_1 + 85x_2 \]

This is because each vase décor brings in a profit of $50 and each wood canvas brings in a profit of $85.

The constraints are based on the time available for carving and polishing:

- For carving: $20x_1 + 18x_2 \leq 400$ (since each vase décor requires 20 minutes for carving, each wood canvas requires 18 minutes, and there are 400 minutes available),
- For polishing: $14x_1 + 8x_2 \leq 640$ (since each vase décor requires 14 minutes for polishing, each wood canvas requires 8 minutes, and there are 640 minutes available).

Additionally, the number of décors produced cannot be negative:
\[ x_1 \geq 0, x_2 \geq 0 \]

In symbolic representation, this problem can be summarized as:
```json
{
  'sym_variables': [('x1', 'number of vase decors'), ('x2', 'number of wood canvases')],
  'objective_function': '50*x1 + 85*x2',
  'constraints': ['20*x1 + 18*x2 <= 400', '14*x1 + 8*x2 <= 640', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we can use the following code:
```python
from gurobipy import *

# Create a new model
m = Model("Wood_Decor_Optimization")

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

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

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

# Optimize the model
m.optimize()

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