To solve the given optimization problem, we first need to identify and formulate the objective function and constraints based on the provided description. The goal is to minimize the cost function which is defined as:

1 * (quantity of peanutbutter sandwiches) + 6 * (quantity of steaks) + 8 * (quantity of strips of bacon)

Subject to several constraints related to calcium intake from different food sources and other conditions.

The constraints are:
1. Calcium from peanutbutter sandwiches, steaks, and strips of bacon should not exceed 32 milligrams.
2. The combination of peanutbutter sandwiches and strips of bacon must provide at least 13 milligrams of calcium.
3. The combination of peanutbutter sandwiches and steaks must provide at least 12 milligrams of calcium.
4. All three sources (peanutbutter sandwiches, steaks, and strips of bacon) together must provide at least 12 milligrams of calcium.
5. A specific relationship between the quantities of steaks and strips of bacon: -10 * (number of steaks) + 1 * (number of strips of bacon) >= 0.
6. Another relationship between peanutbutter sandwiches and strips of bacon: -2 * (number of peanutbutter sandwiches) + 7 * (number of strips of bacon) >= 0.
7. The quantities of peanutbutter sandwiches and steaks must be whole numbers, while the quantity of strips of bacon can be any non-negative real number.

Given these conditions, we'll express this problem using Gurobi Python API:

```python
from gurobipy import *

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

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

# Objective function
m.setObjective(1 * peanutbutter_sandwiches + 6 * steaks + 8 * strips_of_bacon, GRB.MINIMIZE)

# Constraints
# Calcium constraints
m.addConstr(2.0 * peanutbutter_sandwiches + 0.96 * steaks + 1.04 * strips_of_bacon <= 32, name="calcium_upper_bound")
m.addConstr(2.0 * peanutbutter_sandwiches + 1.04 * strips_of_bacon >= 13, name="peanutbutter_and_bacon_calcium")
m.addConstr(2.0 * peanutbutter_sandwiches + 0.96 * steaks >= 12, name="peanutbutter_and_steak_calcium")
m.addConstr(2.0 * peanutbutter_sandwiches + 0.96 * steaks + 1.04 * strips_of_bacon >= 12, name="all_sources_calcium")

# Relationship constraints
m.addConstr(-10 * steaks + 1 * strips_of_bacon >= 0, name="steak_and_bacon_relationship")
m.addConstr(-2 * peanutbutter_sandwiches + 7 * strips_of_bacon >= 0, name="peanutbutter_and_bacon_relationship")

# Non-negativity constraints
m.addConstr(peanutbutter_sandwiches >= 0, name="peanutbutter_non_negative")
m.addConstr(steaks >= 0, name="steak_non_negative")
m.addConstr(strips_of_bacon >= 0, name="bacon_non_negative")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print('Optimal solution found:')
    print(f'peanutbutter_sandwiches = {peanutbutter_sandwiches.x}')
    print(f'steaks = {steaks.x}')
    print(f'strips_of_bacon = {strips_of_bacon.x}')
    print(f'Objective value: {m.objVal}')
else:
    print('No optimal solution found')
```