## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- $x_1$ = strips of bacon
- $x_2$ = cherry pies
- $x_3$ = slices of pizza
- $x_4$ = bagged salads
- $x_5$ = peanutbutter sandwiches

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is: $3.17x_1 + 8.18x_2 + 6.91x_3 + 8.42x_4 + 4.16x_5$

## 3: List the constraints in symbolic notation
The constraints are:
1. $9x_1 + 8x_2 + 10x_3 + 6x_4 + 12x_5 \geq 66$ (total spend)
2. $9x_1 + 8x_2 \geq 27$ (bacon and cherry pies)
3. $9x_1 + 12x_5 \geq 53$ (bacon and peanutbutter sandwiches)
4. $6x_4 + 12x_5 \geq 35$ (bagged salads and peanutbutter sandwiches)
5. $10x_3 + 12x_5 \geq 40$ (pizza and peanutbutter sandwiches)
6. $8x_2 + 12x_5 \geq 66$ (cherry pies and peanutbutter sandwiches)
7. $9x_1 + 8x_2 \leq 143$ (bacon and cherry pies max)
8. $8x_2 + 6x_4 \leq 254$ (cherry pies and bagged salads max)
9. $8x_2 + 6x_4 + 12x_5 \leq 110$ (cherry pies, bagged salads, and peanutbutter sandwiches max)

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'strips of bacon'), 
        ('x2', 'cherry pies'), 
        ('x3', 'slices of pizza'), 
        ('x4', 'bagged salads'), 
        ('x5', 'peanutbutter sandwiches')
    ], 
    'objective_function': '3.17*x1 + 8.18*x2 + 6.91*x3 + 8.42*x4 + 4.16*x5', 
    'constraints': [
        '9*x1 + 8*x2 + 10*x3 + 6*x4 + 12*x5 >= 66',
        '9*x1 + 8*x2 >= 27',
        '9*x1 + 12*x5 >= 53',
        '6*x4 + 12*x5 >= 35',
        '10*x3 + 12*x5 >= 40',
        '8*x2 + 12*x5 >= 66',
        '9*x1 + 8*x2 <= 143',
        '8*x2 + 6*x4 <= 254',
        '8*x2 + 6*x4 + 12*x5 <= 110'
    ]
}
```

## 5: Create the Gurobi model and add variables
We will now create a Gurobi model and add variables with their respective constraints.

```python
import gurobi

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

# Define the variables
x1 = m.addVar(name="strips_of_bacon", lb=0)  # No lower bound specified, but implied 0
x2 = m.addVar(name="cherry_pies", lb=0)  
x3 = m.addVar(name="slices_of_pizza", lb=0, integrality=gurobi.GRB.INTEGER)  # Must be an integer
x4 = m.addVar(name="bagged_salads", lb=0)  
x5 = m.addVar(name="peanutbutter_sandwiches", lb=0, integrality=gurobi.GRB.INTEGER)  # Must be an integer

# Objective function
m.setObjective(3.17*x1 + 8.18*x2 + 6.91*x3 + 8.42*x4 + 4.16*x5, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(9*x1 + 8*x2 + 10*x3 + 6*x4 + 12*x5 >= 66)  
m.addConstr(9*x1 + 8*x2 >= 27)  
m.addConstr(9*x1 + 12*x5 >= 53)  
m.addConstr(6*x4 + 12*x5 >= 35)  
m.addConstr(10*x3 + 12*x5 >= 40)  
m.addConstr(8*x2 + 12*x5 >= 66)  
m.addConstr(9*x1 + 8*x2 <= 143)  
m.addConstr(8*x2 + 6*x4 <= 254)  
m.addConstr(8*x2 + 6*x4 + 12*x5 <= 110)  

# Set dollar cost constraint upper bound
m.addConstr(9*x1 + 8*x2 + 10*x3 + 6*x4 + 12*x5 <= 359)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.Status.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Strips of bacon: ", x1.varValue)
    print("Cherry pies: ", x2.varValue)
    print("Slices of pizza: ", x3.varValue)
    print("Bagged salads: ", x4.varValue)
    print("Peanutbutter sandwiches: ", x5.varValue)
else:
    print("The model is infeasible")
```