## Step 1: Define the optimization problem and its components
The problem is to minimize the objective function: $7 \times \text{chicken thighs} + 5 \times \text{bagged salads} + 5 \times \text{apples} + 9 \times \text{knishes} + 3 \times \text{slices of pizza}$, subject to various constraints related to the milligrams of calcium from each food item.

## 2: List all the constraints
1. $\text{chicken thighs} \times 2 + \text{bagged salads} \times 2 + \text{apples} \times 15 + \text{knishes} \times 16 + \text{slices of pizza} \times 7 \leq 207$
2. $\text{bagged salads} \times 2 + \text{knishes} \times 16 \geq 28$
3. $\text{chicken thighs} \times 2 + \text{apples} \times 15 \geq 37$
4. $\text{apples} \times 15 + \text{knishes} \times 16 \geq 33$
5. $\text{chicken thighs} \times 2 + \text{slices of pizza} \times 7 \geq 37$
6. $\text{knishes} \times 16 + \text{slices of pizza} \times 7 \geq 21$
7. $\text{bagged salads} \times 2 + \text{apples} \times 15 \geq 39$
8. $\text{chicken thighs} \times 2 + \text{knishes} \times 16 + \text{slices of pizza} \times 7 \geq 29$
9. $\text{chicken thighs} \times 2 + \text{bagged salads} \times 2 + \text{apples} \times 15 + \text{knishes} \times 16 + \text{slices of pizza} \times 7 \geq 29$
10. $2 \times \text{knishes} - 10 \times \text{slices of pizza} \geq 0$
11. $\text{apples} \times 15 + \text{knishes} \times 16 \leq 64$
12. $\text{bagged salads} \times 2 + \text{apples} \times 15 + \text{knishes} \times 16 \leq 196$
13. $\text{chicken thighs} \times 2 + \text{bagged salads} \times 2 + \text{slices of pizza} \times 7 \leq 153$
14. $\text{chicken thighs} \times 2 + \text{bagged salads} \times 2 + \text{apples} \times 15 \leq 86$

## 3: Convert the problem into Gurobi code
```python
import gurobi

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

# Define the variables
chicken_thighs = m.addVar(name="chicken_thighs", lb=0)
bagged_salads = m.addVar(name="bagged_salads", lb=0)
apples = m.addVar(name="apples", lb=0)
knishes = m.addVar(name="knishes", lb=0)
slices_of_pizza = m.addVar(name="slices_of_pizza", lb=0)

# Objective function
m.setObjective(7 * chicken_thighs + 5 * bagged_salads + 5 * apples + 9 * knishes + 3 * slices_of_pizza, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(2 * chicken_thighs + 2 * bagged_salads + 15 * apples + 16 * knishes + 7 * slices_of_pizza <= 207, name="calcium_total")
m.addConstr(2 * bagged_salads + 16 * knishes >= 28, name="calcium_bagged_salads_knishes")
m.addConstr(2 * chicken_thighs + 15 * apples >= 37, name="calcium_chicken_thighs_apples")
m.addConstr(15 * apples + 16 * knishes >= 33, name="calcium_apples_knishes")
m.addConstr(2 * chicken_thighs + 7 * slices_of_pizza >= 37, name="calcium_chicken_thighs_pizza")
m.addConstr(16 * knishes + 7 * slices_of_pizza >= 21, name="calcium_knishes_pizza")
m.addConstr(2 * bagged_salads + 15 * apples >= 39, name="calcium_bagged_salads_apples")
m.addConstr(2 * chicken_thighs + 16 * knishes + 7 * slices_of_pizza >= 29, name="calcium_chicken_thighs_knishes_pizza")
m.addConstr(2 * chicken_thighs + 2 * bagged_salads + 15 * apples + 16 * knishes + 7 * slices_of_pizza >= 29, name="calcium_total_2")
m.addConstr(2 * knishes - 10 * slices_of_pizza >= 0, name="knishes_pizza_constraint")
m.addConstr(15 * apples + 16 * knishes <= 64, name="apples_knishes_calcium_limit")
m.addConstr(2 * bagged_salads + 15 * apples + 16 * knishes <= 196, name="bagged_salads_apples_knishes_calcium_limit")
m.addConstr(2 * chicken_thighs + 2 * bagged_salads + 7 * slices_of_pizza <= 153, name="chicken_thighs_bagged_salads_pizza_calcium_limit")
m.addConstr(2 * chicken_thighs + 2 * bagged_salads + 15 * apples <= 86, name="chicken_thighs_bagged_salads_apples_calcium_limit")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.Status.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Chicken thighs: ", chicken_thighs.varValue)
    print("Bagged salads: ", bagged_salads.varValue)
    print("Apples: ", apples.varValue)
    print("Knishes: ", knishes.varValue)
    print("Slices of pizza: ", slices_of_pizza.varValue)
else:
    print("The model is infeasible")
```