## Step 1: Define the symbolic representation of the variables
The variables are 'peanutbutter sandwiches', 'steaks', and 'strips of bacon'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1 \times x_1 + 6 \times x_2 + 8 \times x_3$.

## 3: Define the constraints in symbolic notation
The constraints are:
1. $2.0x_1 + 0.96x_2 + 1.04x_3 \leq 70$
2. $2.0x_1 \geq 13 - 1.04x_3$ (from peanutbutter sandwiches and strips of bacon for calcium)
3. $2.0x_1 + 0.96x_2 \geq 12$ (from peanutbutter sandwiches and steaks for calcium)
4. $2.0x_1 + 0.96x_2 + 1.04x_3 \geq 12$ (from all for calcium)
5. $-10x_2 + x_3 \geq 0$
6. $-2x_1 + 7x_3 \geq 0$
7. $2.0x_1 + 0.96x_2 + 1.04x_3 \leq 32$
8. $x_1$ is an integer
9. $x_2$ is an integer
10. $x_3$ is continuous

## 4: Create the symbolic representation dictionary
```json
{
'sym_variables': [('x1', 'peanutbutter sandwiches'), ('x2', 'steaks'), ('x3', 'strips of bacon')],
'objective_function': '1*x1 + 6*x2 + 8*x3',
'constraints': [
    '2.0*x1 + 0.96*x2 + 1.04*x3 <= 70',
    '2.0*x1 + 1.04*x3 >= 13',
    '2.0*x1 + 0.96*x2 >= 12',
    '2.0*x1 + 0.96*x2 + 1.04*x3 >= 12',
    '-10*x2 + x3 >= 0',
    '-2*x1 + 7*x3 >= 0',
    '2.0*x1 + 0.96*x2 + 1.04*x3 <= 32',
    'x1 is an integer',
    'x2 is an integer'
]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="peanutbutter_sandwiches", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="steaks", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="strips_of_bacon")

    # Objective function
    model.setObjective(1*x1 + 6*x2 + 8*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2.0*x1 + 0.96*x2 + 1.04*x3 <= 70, name="calcium_upper_bound")
    model.addConstr(2.0*x1 + 1.04*x3 >= 13, name="calcium_from_sandwiches_and_bacon")
    model.addConstr(2.0*x1 + 0.96*x2 >= 12, name="calcium_from_sandwiches_and_steaks")
    model.addConstr(2.0*x1 + 0.96*x2 + 1.04*x3 >= 12, name="calcium_from_all")
    model.addConstr(-10*x2 + x3 >= 0, name="steaks_and_bacon_constraint")
    model.addConstr(-2*x1 + 7*x3 >= 0, name="sandwiches_and_bacon_constraint")
    model.addConstr(2.0*x1 + 0.96*x2 + 1.04*x3 <= 32, name="calcium_total_upper_bound")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Peanutbutter sandwiches: ", x1.varValue)
        print("Steaks: ", x2.varValue)
        print("Strips of bacon: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```