## Symbolic Representation

Let's denote:
- $x_1$ as the number of tuna salad sandwiches
- $x_2$ as the number of chicken salad sandwiches

The objective is to maximize profit, where the profit per tuna salad sandwich is $2 and per chicken salad sandwich is $3. Therefore, the objective function is:

Maximize: $2x_1 + 3x_2$

The constraints are:
- Mixing time: $3x_1 + 5x_2 \leq 300$
- Assembling time: $5x_1 + 6x_2 \leq 400$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'tuna salad sandwiches'), ('x2', 'chicken salad sandwiches')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        '3*x1 + 5*x2 <= 300',
        '5*x1 + 6*x2 <= 400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="tuna_salad_sandwiches", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="chicken_salad_sandwiches", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: Maximize 2*x1 + 3*x2
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(3*x1 + 5*x2 <= 300, name="mixing_time_constraint")
    model.addConstr(5*x1 + 6*x2 <= 400, name="assembling_time_constraint")

    # Solve the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Maximum profit: ${2*x1.varValue + 3*x2.varValue:.2f}")
    else:
        print("The model is infeasible.")

solve_optimization_problem()
```