## Step 1: Define the optimization problem
The problem is to minimize the objective function: $5x_0 + 1x_1 + 6x_2 + 9x_3 + 7x_4$, where $x_0$ represents the number of bowls of cereal, $x_1$ represents the number of peanutbutter sandwiches, $x_2$ represents the number of apples, $x_3$ represents the number of rotisserie chickens, and $x_4$ represents the number of oreos.

## Step 2: Define the resources/attributes
The resources/attributes are given as:
- $r_0$: sourness index with an upper bound of 171
- $r_1$: umami index with an upper bound of 228

## 3: Define the coefficients for each variable
The coefficients for each variable are:
- $x_0$: $r_0 = 0.12$, $r_1 = 0.13$
- $x_1$: $r_0 = 1.75$, $r_1 = 0.75$
- $x_2$: $r_0 = 1.66$, $r_1 = 1.43$
- $x_3$: $r_0 = 0.18$, $r_1 = 0.04$
- $x_4$: $r_0 = 1.88$, $r_1 = 0.97$

## 4: Formulate the constraints
The constraints are:
- $1.75x_1 + 1.88x_4 \geq 14$
- $0.12x_0 + 1.88x_4 \geq 21$
- $0.12x_0 + 0.18x_3 \geq 23$
- $0.12x_0 + 1.75x_1 \geq 32$
- $1.66x_2 + 0.18x_3 + 1.88x_4 \geq 33$
- $0.12x_0 + 1.75x_1 + 1.66x_2 + 0.18x_3 + 1.88x_4 \geq 33$
- $0.13x_0 + 0.97x_4 \geq 42$
- $0.04x_3 + 0.97x_4 \geq 35$
- $1.43x_2 + 0.04x_3 \geq 35$
- $0.75x_1 + 0.97x_4 \geq 31$
- $0.13x_0 + 0.04x_3 + 0.97x_4 \geq 44$
- $0.13x_0 + 0.75x_1 + 1.43x_2 \geq 44$
- $0.13x_0 + 0.04x_3 + 0.97x_4 \geq 42$
- $0.13x_0 + 0.75x_1 + 1.43x_2 \geq 42$
- $0.13x_0 + 0.75x_1 + 1.43x_2 + 0.04x_3 + 0.97x_4 \geq 42$
- $5x_2 - 6x_3 \geq 0$
- $-5x_1 + x_3 \geq 0$
- $10x_0 + 10x_3 - 4x_4 \geq 0$
- $0.12x_0 + 1.88x_4 \leq 110$
- $1.75x_1 + 0.18x_3 + 1.88x_4 \leq 89$
- $1.75x_1 + 1.66x_2 + 0.18x_3 \leq 166$
- $1.75x_1 + 1.66x_2 + 1.88x_4 \leq 50$
- $0.12x_0 + 1.75x_1 + 0.18x_3 \leq 55$
- $0.12x_0 + 1.66x_2 + 1.88x_4 \leq 74$
- $0.13x_0 + 0.75x_1 + 0.97x_4 \leq 226$
- $0.75x_1 + 1.43x_2 + 0.97x_4 \leq 196$
- $0.13x_0 + 0.04x_3 + 0.97x_4 \leq 82$
- $1.43x_2 + 0.04x_3 + 0.97x_4 \leq 152$

## 5: Write the Gurobi code
```python
import gurobi

def optimize():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(lb=0, name="bowls of cereal")
    x1 = model.addVar(lb=0, name="peanutbutter sandwiches")
    x2 = model.addVar(lb=0, name="apples")
    x3 = model.addVar(lb=0, name="rotisserie chickens")
    x4 = model.addVar(lb=0, name="oreos")

    # Define the objective function
    model.setObjective(5*x0 + x1 + 6*x2 + 9*x3 + 7*x4, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(1.75*x1 + 1.88*x4 >= 14, name="sourness_index_constraint_1")
    model.addConstr(0.12*x0 + 1.88*x4 >= 21, name="sourness_index_constraint_2")
    model.addConstr(0.12*x0 + 0.18*x3 >= 23, name="sourness_index_constraint_3")
    model.addConstr(0.12*x0 + 1.75*x1 >= 32, name="sourness_index_constraint_4")
    model.addConstr(1.66*x2 + 0.18*x3 + 1.88*x4 >= 33, name="sourness_index_constraint_5")
    model.addConstr(0.12*x0 + 1.75*x1 + 1.66*x2 + 0.18*x3 + 1.88*x4 >= 33, name="sourness_index_constraint_6")
    model.addConstr(0.13*x0 + 0.97*x4 >= 42, name="umami_index_constraint_1")
    model.addConstr(0.04*x3 + 0.97*x4 >= 35, name="umami_index_constraint_2")
    model.addConstr(1.43*x2 + 0.04*x3 >= 35, name="umami_index_constraint_3")
    model.addConstr(0.75*x1 + 0.97*x4 >= 31, name="umami_index_constraint_4")
    model.addConstr(0.13*x0 + 0.04*x3 + 0.97*x4 >= 44, name="umami_index_constraint_5")
    model.addConstr(0.13*x0 + 0.75*x1 + 1.43*x2 >= 44, name="umami_index_constraint_6")
    model.addConstr(0.13*x0 + 0.04*x3 + 0.97*x4 >= 42, name="umami_index_constraint_7")
    model.addConstr(0.13*x0 + 0.75*x1 + 1.43*x2 >= 42, name="umami_index_constraint_8")
    model.addConstr(0.13*x0 + 0.75*x1 + 1.43*x2 + 0.04*x3 + 0.97*x4 >= 42, name="umami_index_constraint_9")
    model.addConstr(5*x2 - 6*x3 >= 0, name="linear_constraint_1")
    model.addConstr(-5*x1 + x3 >= 0, name="linear_constraint_2")
    model.addConstr(10*x0 + 10*x3 - 4*x4 >= 0, name="linear_constraint_3")
    model.addConstr(0.12*x0 + 1.88*x4 <= 110, name="sourness_index_constraint_10")
    model.addConstr(1.75*x1 + 0.18*x3 + 1.88*x4 <= 89, name="sourness_index_constraint_11")
    model.addConstr(1.75*x1 + 1.66*x2 + 0.18*x3 <= 166, name="sourness_index_constraint_12")
    model.addConstr(1.75*x1 + 1.66*x2 + 1.88*x4 <= 50, name="sourness_index_constraint_13")
    model.addConstr(0.12*x0 + 1.75*x1 + 0.18*x3 <= 55, name="sourness_index_constraint_14")
    model.addConstr(0.12*x0 + 1.66*x2 + 1.88*x4 <= 74, name="sourness_index_constraint_15")
    model.addConstr(0.13*x0 + 0.75*x1 + 0.97*x4 <= 226, name="umami_index_constraint_10")
    model.addConstr(0.75*x1 + 1.43*x2 + 0.97*x4 <= 196, name="umami_index_constraint_11")
    model.addConstr(0.13*x0 + 0.04*x3 + 0.97*x4 <= 82, name="umami_index_constraint_12")
    model.addConstr(1.43*x2 + 0.04*x3 + 0.97*x4 <= 152, name="umami_index_constraint_13")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("bowls of cereal: ", x0.varValue)
        print("peanutbutter sandwiches: ", x1.varValue)
        print("apples: ", x2.varValue)
        print("rotisserie chickens: ", x3.varValue)
        print("oreos: ", x4.varValue)
    else:
        print("The model is infeasible")

optimize()
```