## Step 1: Define the symbolic representation of the variables
The variables are 'potatoes', 'fruit salads', and 'hot dogs', which we will represent symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to minimize is $3.74x_0 + 5.88x_1 + 8.09x_2$.

## Step 3: List all the constraints in symbolic notation
The constraints are:
- $9x_0 + 9x_1 \geq 29$
- $9x_1 + 3x_2 \geq 30$
- $19x_0 + 9x_1 + 3x_2 \geq 46$
- $19x_0 + 9x_1 + 3x_2 \geq 46$ (same as the previous one, so we keep only one)
- $18x_0 + 10x_2 \geq 30$
- $18x_0 + 16x_1 + 10x_2 \geq 30$
- $6x_0 + 3x_1 \geq 29$
- $3x_1 + 17x_2 \geq 41$
- $6x_0 + 17x_2 \geq 17$
- $6x_0 + 3x_1 + 17x_2 \geq 17$
- $26x_1 + 22x_2 \geq 37$
- $x_0 + 22x_2 \geq 23$
- $x_0 + 26x_1 + 22x_2 \geq 23$
- $x_1 - 5x_2 \geq 0$
- $9x_1 + 3x_2 \leq 95$
- $6x_0 + 3x_1 + 17x_2 \leq 57$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'potatoes'), 
        ('x1', 'fruit salads'), 
        ('x2', 'hot dogs')
    ], 
    'objective_function': '3.74*x0 + 5.88*x1 + 8.09*x2', 
    'constraints': [
        '9*x0 + 9*x1 >= 29',
        '9*x1 + 3*x2 >= 30',
        '19*x0 + 9*x1 + 3*x2 >= 46',
        '18*x0 + 10*x2 >= 30',
        '18*x0 + 16*x1 + 10*x2 >= 30',
        '6*x0 + 3*x1 >= 29',
        '3*x1 + 17*x2 >= 41',
        '6*x0 + 17*x2 >= 17',
        '6*x0 + 3*x1 + 17*x2 >= 17',
        '26*x1 + 22*x2 >= 37',
        'x0 + 22*x2 >= 23',
        'x0 + 26*x1 + 22*x2 >= 23',
        'x1 - 5*x2 >= 0',
        '9*x1 + 3*x2 <= 95',
        '6*x0 + 3*x1 + 17*x2 <= 57'
    ]
}
```

## 5: Write the Gurobi code for the optimization problem
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    potatoes = model.addVar(name="potatoes", lb=0)
    fruit_salads = model.addVar(name="fruit_salads", lb=0)
    hot_dogs = model.addVar(name="hot_dogs", lb=0, integrality=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(3.74 * potatoes + 5.88 * fruit_salads + 8.09 * hot_dogs, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(9 * potatoes + 9 * fruit_salads >= 29)
    model.addConstr(9 * fruit_salads + 3 * hot_dogs >= 30)
    model.addConstr(19 * potatoes + 9 * fruit_salads + 3 * hot_dogs >= 46)
    model.addConstr(18 * potatoes + 10 * hot_dogs >= 30)
    model.addConstr(18 * potatoes + 16 * fruit_salads + 10 * hot_dogs >= 30)
    model.addConstr(6 * potatoes + 3 * fruit_salads >= 29)
    model.addConstr(3 * fruit_salads + 17 * hot_dogs >= 41)
    model.addConstr(6 * potatoes + 17 * hot_dogs >= 17)
    model.addConstr(6 * potatoes + 3 * fruit_salads + 17 * hot_dogs >= 17)
    model.addConstr(26 * fruit_salads + 22 * hot_dogs >= 37)
    model.addConstr(potatoes + 22 * hot_dogs >= 23)
    model.addConstr(potatoes + 26 * fruit_salads + 22 * hot_dogs >= 23)
    model.addConstr(fruit_salads - 5 * hot_dogs >= 0)
    model.addConstr(9 * fruit_salads + 3 * hot_dogs <= 95)
    model.addConstr(6 * potatoes + 3 * fruit_salads + 17 * hot_dogs <= 57)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Potatoes: {potatoes.varValue}")
        print(f"Fruit Salads: {fruit_salads.varValue}")
        print(f"Hot Dogs: {hot_dogs.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```