## Symbolic Representation

Let's denote the number of packages of seafood medley one as $x_1$ and the number of packages of seafood medley two as $x_2$. The objective is to maximize profit, which can be represented as $20x_1 + 25x_2$.

The constraints are:
- Scallops: $20x_1 + 40x_2 \leq 10000$
- Mussels: $30x_1 + 40x_2 \leq 12000$
- Oysters: $50x_1 + 20x_2 \leq 11000$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'seafood medley one'), ('x2', 'seafood medley two')],
    'objective_function': '20*x1 + 25*x2',
    'constraints': [
        '20*x1 + 40*x2 <= 10000',
        '30*x1 + 40*x2 <= 12000',
        '50*x1 + 20*x2 <= 11000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Seafood_Medley_Optimization")

# Define variables
x1 = model.addVar(name="seafood_medley_one", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="seafood_medley_two", lb=0, vtype=gp.GRB.CONTINUOUS)

# Define objective function
model.setObjective(20*x1 + 25*x2, gp.GRB.MAXIMIZE)

# Define constraints
model.addConstr(20*x1 + 40*x2 <= 10000, name="scallops_constraint")
model.addConstr(30*x1 + 40*x2 <= 12000, name="mussels_constraint")
model.addConstr(50*x1 + 20*x2 <= 11000, name="oysters_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: seafood medley one = {x1.varValue}, seafood medley two = {x2.varValue}")
    print(f"Maximum profit: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```