## Step 1: Define the symbolic representation of the variables
The variables are 'strips of bacon', 'cheeseburgers', 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:
- $x_0$ : strips of bacon
- $x_1$ : cheeseburgers
- $x_2$ : potatoes

## 3: Define the objective function in symbolic notation
The objective function to minimize is $6x_0 + 7x_1 + 4x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $13x_0 + 3x_2 \geq 30$
2. $13x_0 + 9x_1 + 3x_2 \geq 30$
3. $4x_1 + 4x_2 \geq 32$
4. $14x_0 + 4x_1 + 4x_2 \geq 32$
5. $24x_0 + 21x_2 \geq 28$
6. $24x_0 + 10x_1 + 21x_2 \geq 28$
7. $3x_0 - 6x_1 \geq 0$
8. $-4x_0 + 6x_2 \geq 0$
9. $9x_1 + 3x_2 \leq 136$
10. $13x_0 + 9x_1 + 3x_2 \leq 86$
11. $4x_1 + 4x_2 \leq 95$
12. $14x_0 + 4x_1 \leq 74$
13. $10x_1 + 21x_2 \leq 212$

## 5: Define the upper bounds for resources
The upper bounds for resources are:
- $r_0$ (sourness index): $13x_0 + 9x_1 + 3x_2 \leq 147$
- $r_1$ (dollar cost): $14x_0 + 4x_1 + 4x_2 \leq 97$
- $r_2$ (grams of fat): $24x_0 + 10x_1 + 21x_2 \leq 244$

## 6: Create the symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'strips of bacon'), ('x1', 'cheeseburgers'), ('x2', 'potatoes')],
'objective_function': '6*x0 + 7*x1 + 4*x2',
'constraints': [
'13*x0 + 3*x2 >= 30',
'13*x0 + 9*x1 + 3*x2 >= 30',
'4*x1 + 4*x2 >= 32',
'14*x0 + 4*x1 + 4*x2 >= 32',
'24*x0 + 21*x2 >= 28',
'24*x0 + 10*x1 + 21*x2 >= 28',
'3*x0 - 6*x1 >= 0',
'-4*x0 + 6*x2 >= 0',
'9*x1 + 3*x2 <= 136',
'13*x0 + 9*x1 + 3*x2 <= 86',
'4*x1 + 4*x2 <= 95',
'14*x0 + 4*x1 <= 74',
'10*x1 + 21*x2 <= 212',
'13*x0 + 9*x1 + 3*x2 <= 147',
'14*x0 + 4*x1 + 4*x2 <= 97',
'24*x0 + 10*x1 + 21*x2 <= 244'
]
}
```

## 7: Implement the optimization problem using Gurobi
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="x0", lb=0)  # strips of bacon
x1 = model.addVar(name="x1", lb=0)  # cheeseburgers
x2 = model.addVar(name="x2", lb=0)  # potatoes

# Define the objective function
model.setObjective(6 * x0 + 7 * x1 + 4 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(13 * x0 + 3 * x2 >= 30)
model.addConstr(13 * x0 + 9 * x1 + 3 * x2 >= 30)
model.addConstr(4 * x1 + 4 * x2 >= 32)
model.addConstr(14 * x0 + 4 * x1 + 4 * x2 >= 32)
model.addConstr(24 * x0 + 21 * x2 >= 28)
model.addConstr(24 * x0 + 10 * x1 + 21 * x2 >= 28)
model.addConstr(3 * x0 - 6 * x1 >= 0)
model.addConstr(-4 * x0 + 6 * x2 >= 0)
model.addConstr(9 * x1 + 3 * x2 <= 136)
model.addConstr(13 * x0 + 9 * x1 + 3 * x2 <= 86)
model.addConstr(4 * x1 + 4 * x2 <= 95)
model.addConstr(14 * x0 + 4 * x1 <= 74)
model.addConstr(10 * x1 + 21 * x2 <= 212)
model.addConstr(13 * x0 + 9 * x1 + 3 * x2 <= 147)
model.addConstr(14 * x0 + 4 * x1 + 4 * x2 <= 97)
model.addConstr(24 * x0 + 10 * x1 + 21 * x2 <= 244)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("x0 (strips of bacon): ", x0.varValue)
    print("x1 (cheeseburgers): ", x1.varValue)
    print("x2 (potatoes): ", x2.varValue)
else:
    print("The model is infeasible")
```