## Step 1: Define the symbolic representation of the variables
The variables are 'rotisserie chickens', 'ham sandwiches', and 'potatoes', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : 'rotisserie chickens'
- $x_1$ : 'ham sandwiches'
- $x_2$ : 'potatoes'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $2.4x_0 + 2.19x_1 + 6.37x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $15x_0 + 6x_1 + 15x_2 \geq 28$ (calcium from all sources)
- $17x_1 + 21x_2 \geq 25$ (fat from ham sandwiches and potatoes)
- $20x_0 + 21x_2 \geq 38$ (fat from rotisserie chickens and potatoes)
- $20x_0 + 17x_1 + 21x_2 \geq 35$ (fat from all sources)
- $5x_0 + 14x_1 \geq 13$ (carbohydrates from rotisserie chickens and ham sandwiches)
- $14x_1 + 8x_2 \geq 14$ (carbohydrates from ham sandwiches and potatoes)
- $5x_0 + 8x_2 \geq 14$ (carbohydrates from rotisserie chickens and potatoes)
- $15x_0 + 6x_1 \leq 40$ (calcium from rotisserie chickens and ham sandwiches)
- $15x_0 + 6x_1 + 15x_2 \leq 40$ is not valid, instead $15x_0 + 6x_1 + 15x_2 \leq 122$ (calcium from all sources has an upper bound of 122)
- $17x_1 + 21x_2 \leq 77$ (fat from ham sandwiches and potatoes)
- $20x_0 + 17x_1 \leq 105$ (fat from rotisserie chickens and ham sandwiches)
- $20x_0 + 21x_2 \leq 114$ (fat from rotisserie chickens and potatoes)
- $20x_0 + 17x_1 + 21x_2 \leq 114$ is not valid, instead $20x_0 + 17x_1 + 21x_2 \leq 143$ (fat from all sources has an upper bound of 143)
- $5x_0 + 14x_1 \leq 63$ (carbohydrates from rotisserie chickens and ham sandwiches)
- $14x_1 + 8x_2 \leq 60$ (carbohydrates from ham sandwiches and potatoes)
- $5x_0 + 14x_1 + 8x_2 \leq 60$ (carbohydrates from all sources)

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'rotisserie chickens'), ('x1', 'ham sandwiches'), ('x2', 'potatoes')],
    'objective_function': '2.4*x0 + 2.19*x1 + 6.37*x2',
    'constraints': [
        '15*x0 + 6*x1 + 15*x2 >= 28',
        '17*x1 + 21*x2 >= 25',
        '20*x0 + 21*x2 >= 38',
        '20*x0 + 17*x1 + 21*x2 >= 35',
        '5*x0 + 14*x1 >= 13',
        '14*x1 + 8*x2 >= 14',
        '5*x0 + 8*x2 >= 14',
        '15*x0 + 6*x1 <= 40',
        '15*x0 + 6*x1 + 15*x2 <= 122',
        '17*x1 + 21*x2 <= 77',
        '20*x0 + 17*x1 <= 105',
        '20*x0 + 21*x2 <= 114',
        '20*x0 + 17*x1 + 21*x2 <= 143',
        '5*x0 + 14*x1 <= 63',
        '14*x1 + 8*x2 <= 60',
        '5*x0 + 14*x1 + 8*x2 <= 60'
    ]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name='rotisserie_chickens', lb=0)
    x1 = model.addVar(name='ham_sandwiches', lb=0)
    x2 = model.addVar(name='potatoes', lb=0)

    # Define the objective function
    model.setObjective(2.4 * x0 + 2.19 * x1 + 6.37 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(15 * x0 + 6 * x1 + 15 * x2 >= 28)
    model.addConstr(17 * x1 + 21 * x2 >= 25)
    model.addConstr(20 * x0 + 21 * x2 >= 38)
    model.addConstr(20 * x0 + 17 * x1 + 21 * x2 >= 35)
    model.addConstr(5 * x0 + 14 * x1 >= 13)
    model.addConstr(14 * x1 + 8 * x2 >= 14)
    model.addConstr(5 * x0 + 8 * x2 >= 14)
    model.addConstr(15 * x0 + 6 * x1 <= 40)
    model.addConstr(15 * x0 + 6 * x1 + 15 * x2 <= 122)
    model.addConstr(17 * x1 + 21 * x2 <= 77)
    model.addConstr(20 * x0 + 17 * x1 <= 105)
    model.addConstr(20 * x0 + 21 * x2 <= 114)
    model.addConstr(20 * x0 + 17 * x1 + 21 * x2 <= 143)
    model.addConstr(5 * x0 + 14 * x1 <= 63)
    model.addConstr(14 * x1 + 8 * x2 <= 60)
    model.addConstr(5 * x0 + 14 * x1 + 8 * x2 <= 60)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('rotisserie chickens: ', x0.varValue)
        print('ham sandwiches: ', x1.varValue)
        print('potatoes: ', x2.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```