## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of oval pots
- $x_2$ represents the number of square pots

The objective function to maximize profit is: $4.5x_1 + 8x_2$

The constraints based on the given resources are:
- Shaping time: $40x_1 + 35x_2 \leq 2500$
- Baking time: $50x_1 + 80x_2 \leq 3800$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi code

To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

## Step 3: Write down the Gurobi code

```python
import gurobi as gp

# Create a new model
m = gp.Model()

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

# Set the objective function
m.setObjective(4.5 * x1 + 8 * x2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(40 * x1 + 35 * x2 <= 2500, name="shaping_time")
m.addConstr(50 * x1 + 80 * x2 <= 3800, name="baking_time")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of oval pots: {x1.varValue}")
    print(f"Number of square pots: {x2.varValue}")
    print(f"Maximal profit: {m.objVal}")
else:
    print("No optimal solution found.")
```

## Step 4: Provide the symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'oval pots'), ('x2', 'square pots')],
    'objective_function': '4.5*x1 + 8*x2',
    'constraints': [
        '40*x1 + 35*x2 <= 2500',
        '50*x1 + 80*x2 <= 3800',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```