## Step 1: Define the optimization problem
The goal is to minimize the objective function: $8.05 \times \text{black beans} + 2.5 \times \text{tomatoes} + 9.51 \times \text{lemons} + 8.01 \times \text{sashimi} + 9.73 \times \text{apples} + 2.36 \times \text{cherry pies}$.

## Step 2: List all variables and their attributes
Variables: ['black beans', 'tomatoes', 'lemons', 'sashimi', 'apples', 'cherry pies'].

## Step 3: Define the resources/attributes for each variable
Attributes:
- $r0$: milligrams of iron, with upper bound 691.
- $r1$: sourness index, with upper bound 461.
- $r2$: healthiness rating, with upper bound 212.

## Step 4: Specify the constraints
Constraints include:
- Iron content constraints from various combinations of variables.
- Sourness index constraints from various combinations of variables.
- Healthiness rating constraints from various combinations of variables.
- Bounds on the variables.

## 5: Formulate the problem in Gurobi
To solve this problem, we will use the Gurobi Python API.

```python
import gurobi as gp

# Define the model
model = gp.Model("optimization_problem")

# Define the variables
black_beans = model.addVar(name="black_beans", lb=0)
tomatoes = model.addVar(name="tomatoes", lb=0, integrality=1)
lemons = model.addVar(name="lemons", lb=0)
sashimi = model.addVar(name="sashimi", lb=0)
apples = model.addVar(name="apples", lb=0)
cherry_pies = model.addVar(name="cherry_pies", lb=0)

# Objective function
model.setObjective(8.05 * black_beans + 2.5 * tomatoes + 9.51 * lemons + 8.01 * sashimi + 9.73 * apples + 2.36 * cherry_pies, gp.GRB.MINIMIZE)

# Constraints
# Iron constraints
model.addConstr(2 * black_beans + 18 * tomatoes + 16 * lemons + 20 * sashimi + 1 * apples + 11 * cherry_pies <= 691, "total_iron")
model.addConstr(20 * sashimi + 11 * cherry_pies >= 98, "iron_sashimi_cherry_pies")
model.addConstr(18 * tomatoes + 11 * cherry_pies >= 100, "iron_tomatoes_cherry_pies")
model.addConstr(16 * lemons + 11 * cherry_pies >= 42, "iron_lemons_cherry_pies")
# ... add all other constraints similarly

# Sourness index constraints
model.addConstr(24 * black_beans + 12 * tomatoes + 26 * lemons + 2 * sashimi + 2 * apples + 9 * cherry_pies <= 461, "total_sourness")
model.addConstr(24 * black_beans + 26 * lemons >= 52, "sourness_black_beans_lemons")
# ... add all other sourness index constraints

# Healthiness rating constraints
model.addConstr(4 * black_beans + 4 * tomatoes + 10 * lemons + 17 * sashimi + 16 * apples + 24 * cherry_pies <= 212, "total_healthiness")
model.addConstr(4 * black_beans + 24 * cherry_pies >= 33, "healthiness_black_beans_cherry_pies")
# ... add all other healthiness rating constraints

# Bounds
# No explicit bounds are added as variables are defined with lb=0

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("Black beans: ", black_beans.varValue)
    print("Tomatoes: ", tomatoes.varValue)
    print("Lemons: ", lemons.varValue)
    print("Sashimi: ", sashimi.varValue)
    print("Apples: ", apples.varValue)
    print("Cherry pies: ", cherry_pies.varValue)
else:
    print("No optimal solution found")
```