## Step 1: Define the optimization problem and identify the variables, objective function, and constraints.
The problem involves minimizing an objective function subject to several constraints. The variables are 'kale salads', 'tomatoes', and 'strips of bacon'. The objective function to minimize is $9 \times \text{kale salads} + 1 \times \text{tomatoes} + 6 \times \text{strips of bacon}$.

## 2: List the constraints based on the problem description.
1. $\text{tastiness rating of kale salads} = 3$
2. $\text{calcium in kale salads} = 8$ mg
3. $\text{protein in kale salads} = 7$ grams
4. $\text{tastiness rating of tomatoes} = 14$
5. $\text{calcium in tomatoes} = 13$ mg
6. $\text{protein in tomatoes} = 5$ grams
7. $\text{tastiness rating of strips of bacon} = 2$
8. $\text{calcium in strips of bacon} = 8$ mg
9. $\text{protein in strips of bacon} = 3$ grams
10. Total tastiness rating from kale salads and tomatoes $\geq 20$
11. Total tastiness rating from tomatoes and strips of bacon $\geq 13$
12. Total tastiness rating from kale salads, tomatoes, and strips of bacon $\geq 21$
13. Total calcium from kale salads and tomatoes $\geq 51$ mg
14. Total calcium from tomatoes and strips of bacon $\geq 45$ mg
15. Total calcium from kale salads and strips of bacon $\geq 21$ mg
16. Total calcium from kale salads, tomatoes, and strips of bacon $\geq 21$ mg
17. Total protein from kale salads and tomatoes $\geq 57$ grams
18. Total protein from kale salads, tomatoes, and strips of bacon $\geq 57$ grams
19. $-5 \times \text{tomatoes} + 4 \times \text{strips of bacon} \geq 0$
20. $-6 \times \text{kale salads} + 9 \times \text{tomatoes} \geq 0$
21. Total tastiness rating from kale salads and tomatoes $\leq 48$
22. $\text{kale salads}$, $\text{tomatoes}$, and $\text{strips of bacon}$ must be integers.

## 3: Formulate the problem in Gurobi.
We will use Gurobi's Python interface to model and solve this problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
kale_salads = m.addVar(name="kale_salads", vtype=gp.GRB.INTEGER)
tomatoes = m.addVar(name="tomatoes", vtype=gp.GRB.INTEGER)
strips_of_bacon = m.addVar(name="strips_of_bacon", vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(9 * kale_salads + tomatoes + 6 * strips_of_bacon, gp.GRB.MINIMIZE)

# Constraints
m.addConstr(3 * kale_salads + 14 * tomatoes >= 20)  # 10
m.addConstr(14 * tomatoes + 2 * strips_of_bacon >= 13)  # 11
m.addConstr(3 * kale_salads + 14 * tomatoes + 2 * strips_of_bacon >= 21)  # 12
m.addConstr(8 * kale_salads + 13 * tomatoes >= 51)  # 13
m.addConstr(13 * tomatoes + 8 * strips_of_bacon >= 45)  # 14
m.addConstr(8 * kale_salads + 8 * strips_of_bacon >= 21)  # 15
m.addConstr(8 * kale_salads + 13 * tomatoes + 8 * strips_of_bacon >= 21)  # 16
m.addConstr(7 * kale_salads + 5 * tomatoes >= 57)  # 17
m.addConstr(7 * kale_salads + 5 * tomatoes + 3 * strips_of_bacon >= 57)  # 18
m.addConstr(-5 * tomatoes + 4 * strips_of_bacon >= 0)  # 19
m.addConstr(-6 * kale_salads + 9 * tomatoes >= 0)  # 20
m.addConstr(3 * kale_salads + 14 * tomatoes <= 48)  # 21

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Kale Salads: ", kale_salads.varValue)
    print("Tomatoes: ", tomatoes.varValue)
    print("Strips of Bacon: ", strips_of_bacon.varValue)
else:
    print("The model is infeasible")
```