To solve Eric's problem, we need to define the variables and constraints of the optimization problem. Let's denote the number of display shelves as $x$ and the number of plant stands as $y$. 

The objective function is to maximize profit: $55x + 45y$, since each display shelf brings in $55 and each plant stand brings in $45.

There are two main constraints:
1. Carving time constraint: The total carving time for both products should not exceed 350 minutes. Since a display shelf requires 25 minutes of carving and a plant stand requires 20 minutes, the constraint is $25x + 20y \leq 350$.
2. Polishing time constraint: The total polishing time should not exceed 600 minutes. Given that a display shelf requires 20 minutes for polishing and a plant stand requires 10 minutes, this constraint is $20x + 10y \leq 600$.

Additionally, we have non-negativity constraints because the number of decors cannot be negative: $x \geq 0$ and $y \geq 0$.

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

```python
from gurobipy import *

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

# Define variables
x = m.addVar(vtype=GRB.INTEGER, name="display_shelves")
y = m.addVar(vtype=GRB.INTEGER, name="plant_stands")

# Set the objective function: Maximize profit
m.setObjective(55*x + 45*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(25*x + 20*y <= 350, "carving_time")
m.addConstr(20*x + 10*y <= 600, "polishing_time")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Display Shelves: {x.x}")
    print(f"Plant Stands: {y.x}")
    print(f"Maximum Profit: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```