## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'blueberry pies', 'milkshakes', and 'hot dogs', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $1.23x_0 + 2.93x_1 + 7.79x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $16x_0 + 19x_1 + 6x_2 \leq 272$ (carbohydrates total)
- $11x_0 + 25x_1 + 24x_2 \leq 280$ (fat total)
- $22x_0 + 19x_1 + 23x_2 \leq 259$ (calcium total)
- $19x_1 + 6x_2 \geq 49$ (carbohydrates from milkshakes and hot dogs)
- $16x_0 + 19x_1 \geq 63$ (carbohydrates from blueberry pies and milkshakes)
- $11x_0 + 25x_1 \geq 54$ (fat from blueberry pies and milkshakes)
- $25x_1 + 24x_2 \geq 80$ (fat from milkshakes and hot dogs)
- $22x_0 + 19x_1 \geq 67$ (calcium from blueberry pies and milkshakes)
- $19x_1 + 6x_2 \leq 231$ (carbohydrates from milkshakes and hot dogs upper bound)
- $16x_0 + 19x_1 \leq 198$ (carbohydrates from blueberry pies and milkshakes upper bound)
- $16x_0 + 19x_1 + 6x_2 \leq 198$ (carbohydrates total upper bound, redundant with first constraint)
- $11x_0 + 24x_2 \leq 134$ (fat from blueberry pies and hot dogs)
- $25x_1 + 24x_2 \leq 186$ (fat from milkshakes and hot dogs upper bound)
- $11x_0 + 25x_1 + 24x_2 \leq 186$ (fat total upper bound, redundant with second constraint)
- $22x_0 + 23x_2 \leq 242$ (calcium from blueberry pies and hot dogs)
- $22x_0 + 19x_1 \leq 206$ (calcium from blueberry pies and milkshakes)
- $22x_0 + 19x_1 + 23x_2 \leq 206$ (calcium total upper bound)

## 4: Create the symbolic representation dictionary
```json
{
    'sym_variables': [
        ('x0', 'blueberry pies'),
        ('x1', 'milkshakes'),
        ('x2', 'hot dogs')
    ],
    'objective_function': '1.23*x0 + 2.93*x1 + 7.79*x2',
    'constraints': [
        '16*x0 + 19*x1 + 6*x2 <= 272',
        '11*x0 + 25*x1 + 24*x2 <= 280',
        '22*x0 + 19*x1 + 23*x2 <= 259',
        '19*x1 + 6*x2 >= 49',
        '16*x0 + 19*x1 >= 63',
        '11*x0 + 25*x1 >= 54',
        '25*x1 + 24*x2 >= 80',
        '22*x0 + 19*x1 >= 67',
        '19*x1 + 6*x2 <= 231',
        '16*x0 + 19*x1 <= 198',
        '11*x0 + 24*x2 <= 134',
        '25*x1 + 24*x2 <= 186',
        '22*x0 + 23*x2 <= 242',
        '22*x0 + 19*x1 <= 206'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name='blueberry_pies', lb=0)
    x1 = model.addVar(name='milkshakes', lb=0)
    x2 = model.addVar(name='hot_dogs', lb=0)

    # Define the objective function
    model.setObjective(1.23*x0 + 2.93*x1 + 7.79*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(16*x0 + 19*x1 + 6*x2 <= 272)  # carbohydrates total
    model.addConstr(11*x0 + 25*x1 + 24*x2 <= 280)  # fat total
    model.addConstr(22*x0 + 19*x1 + 23*x2 <= 259)  # calcium total
    model.addConstr(19*x1 + 6*x2 >= 49)  # carbohydrates from milkshakes and hot dogs
    model.addConstr(16*x0 + 19*x1 >= 63)  # carbohydrates from blueberry pies and milkshakes
    model.addConstr(11*x0 + 25*x1 >= 54)  # fat from blueberry pies and milkshakes
    model.addConstr(25*x1 + 24*x2 >= 80)  # fat from milkshakes and hot dogs
    model.addConstr(22*x0 + 19*x1 >= 67)  # calcium from blueberry pies and milkshakes
    model.addConstr(19*x1 + 6*x2 <= 231)  # carbohydrates from milkshakes and hot dogs upper bound
    model.addConstr(16*x0 + 19*x1 <= 198)  # carbohydrates from blueberry pies and milkshakes upper bound
    model.addConstr(11*x0 + 24*x2 <= 134)  # fat from blueberry pies and hot dogs
    model.addConstr(25*x1 + 24*x2 <= 186)  # fat from milkshakes and hot dogs upper bound
    model.addConstr(22*x0 + 23*x2 <= 242)  # calcium from blueberry pies and hot dogs
    model.addConstr(22*x0 + 19*x1 <= 206)  # calcium from blueberry pies and milkshakes

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Blueberry pies: ', x0.varValue)
        print('Milkshakes: ', x1.varValue)
        print('Hot dogs: ', x2.varValue)
    else:
        print('No optimal solution found')

solve_optimization_problem()
```