To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints in terms of these variables.

Let's define the variables as follows:
- \(x_1\): quantity of peanutbutter sandwiches
- \(x_2\): quantity of steaks
- \(x_3\): quantity of strips of bacon

The objective function to minimize is: \(1 \cdot x_1 + 6 \cdot x_2 + 8 \cdot x_3\)

The constraints are:
1. Calcium from peanutbutter sandwiches and strips of bacon: \(2.0x_1 + 1.04x_3 \geq 13\)
2. Calcium from peanutbutter sandwiches and steaks: \(2.0x_1 + 0.96x_2 \geq 12\)
3. Calcium from all sources: \(2.0x_1 + 0.96x_2 + 1.04x_3 \geq 12\)
4. Constraint involving steaks and strips of bacon: \(-10x_2 + x_3 \geq 0\)
5. Constraint involving peanutbutter sandwiches and strips of bacon: \(-2x_1 + 7x_3 \geq 0\)
6. Total calcium constraint: \(2.0x_1 + 0.96x_2 + 1.04x_3 \leq 32\)

Given the problem's requirements, we also note:
- \(x_1\) and \(x_2\) must be integers (whole numbers of peanutbutter sandwiches and steaks).
- \(x_3\) can be any non-negative real number (quantity of strips of bacon).

The symbolic representation of the problem is thus:
```json
{
  'sym_variables': [('x1', 'peanutbutter sandwiches'), ('x2', 'steaks'), ('x3', 'strips of bacon')],
  'objective_function': '1*x1 + 6*x2 + 8*x3',
  'constraints': [
    '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'
  ]
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

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

# Set the objective function
m.setObjective(1*x1 + 6*x2 + 8*x3, GRB.MINIMIZE)

# Add the constraints
m.addConstr(2.0*x1 + 1.04*x3 >= 13, "calcium_from_pb_and_bacon")
m.addConstr(2.0*x1 + 0.96*x2 >= 12, "calcium_from_pb_and_steaks")
m.addConstr(2.0*x1 + 0.96*x2 + 1.04*x3 >= 12, "total_calcium_lower_bound")
m.addConstr(-10*x2 + x3 >= 0, "steaks_and_bacon_constraint")
m.addConstr(-2*x1 + 7*x3 >= 0, "pb_and_bacon_constraint")
m.addConstr(2.0*x1 + 0.96*x2 + 1.04*x3 <= 32, "total_calcium_upper_bound")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"peanutbutter sandwiches: {x1.x}")
    print(f"steaks: {x2.x}")
    print(f"strips of bacon: {x3.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```