To solve Eric's problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of display shelves as $x_1$ and the number of plant stands as $x_2$. The objective is to maximize profit, which can be represented by the function $55x_1 + 45x_2$, since each display shelf brings in $55 and each plant stand brings in $45.

The constraints are based on the time available for carving and polishing:
- Carving: $25x_1 + 20x_2 \leq 350$ (since each display shelf requires 25 minutes of carving and each plant stand requires 20 minutes, and there are 350 minutes available).
- Polishing: $20x_1 + 10x_2 \leq 600$ (since each display shelf requires 20 minutes of polishing and each plant stand requires 10 minutes, and there are 600 minutes available).

Additionally, we have non-negativity constraints since Eric cannot produce a negative number of decors:
- $x_1 \geq 0$
- $x_2 \geq 0$

So, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of display shelves'), ('x2', 'number of plant stands')],
    'objective_function': '55*x1 + 45*x2',
    'constraints': ['25*x1 + 20*x2 <= 350', '20*x1 + 10*x2 <= 600', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="display_shelves", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="plant_stands", lb=0)

# Set the objective function
m.setObjective(55*x1 + 45*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(25*x1 + 20*x2 <= 350, "carving_time")
m.addConstr(20*x1 + 10*x2 <= 600, "polishing_time")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.x}, x2 = {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```