## Step 1: Define the variables and the objective function
The variables are ['milligrams of vitamin B6', 'milligrams of potassium', 'grams of carbohydrates', 'milligrams of vitamin B4']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively. The objective function to maximize is $8.98x_0 + 1.03x_1 + 7.98x_2 + 3.6x_3$.

## Step 2: Define the constraints
The constraints are given based on the resources/attributes. Let's list them out:
- $2x_0 + 5x_1 + 5x_2 + 2x_3 \leq 77$ (immune support index)
- $8x_0 + 5x_1 + 5x_2 + 3x_3 \leq 64$ (kidney support index)
- $8x_0 + 3x_1 + x_2 + 5x_3 \leq 80$ (digestive support index)
- $5x_1 + 2x_3 \geq 12$ (immune support index from $x_1$ and $x_3$)
- $5x_2 + 2x_3 \geq 13$ (immune support index from $x_2$ and $x_3$)
- $2x_0 + 5x_1 + 2x_3 \geq 10$ (immune support index from $x_0$, $x_1$, and $x_3$)
- $2x_0 + 5x_2 + 2x_3 \geq 10$ (immune support index from $x_0$, $x_2$, and $x_3$)
- $2x_0 + 5x_1 + 2x_3 \geq 15$ (immune support index from $x_0$, $x_1$, and $x_3$)
- $2x_0 + 5x_2 + 2x_3 \geq 15$ (immune support index from $x_0$, $x_2$, and $x_3$)
- $8x_0 + 5x_1 + 5x_2 \geq 14$ (kidney support index from $x_0$, $x_1$, and $x_2$)
- $8x_0 + 3x_1 + x_2 \geq 16$ (digestive support index from $x_0$, $x_1$, and $x_2$)
- $8x_0 + x_2 + 5x_3 \geq 16$ (digestive support index from $x_0$, $x_2$, and $x_3$)
- $8x_0 + 3x_1 + x_2 \geq 12$ (digestive support index from $x_0$, $x_1$, and $x_2$)
- $8x_0 + x_2 + 5x_3 \geq 12$ (digestive support index from $x_0$, $x_2$, and $x_3$)
- $5x_1 + 2x_3 \leq 52$ (immune support index from $x_1$ and $x_3$)
- $2x_0 + 5x_1 + 5x_2 + 2x_3 \leq 52$ (immune support index from all variables)
- $8x_0 + 5x_1 \leq 37$ (kidney support index from $x_0$ and $x_1$)
- $8x_0 + 5x_2 \leq 55$ (kidney support index from $x_0$ and $x_2$)
- $5x_1 + 5x_2 \leq 52$ (kidney support index from $x_1$ and $x_2$)
- $5x_1 + 3x_3 \leq 53$ (kidney support index from $x_1$ and $x_3$)
- $5x_1 + 5x_2 + 3x_3 \leq 28$ (kidney support index from $x_1$, $x_2$, and $x_3$)
- $8x_0 + 5x_1 + 5x_2 + 3x_3 \leq 28$ (kidney support index from all variables)
- $3x_1 + x_2 \leq 64$ (digestive support index from $x_1$ and $x_2$)
- $x_2 + 5x_3 \leq 28$ (digestive support index from $x_2$ and $x_3$)
- $8x_0 + x_2 \leq 56$ (digestive support index from $x_0$ and $x_2$)
- $3x_1 + 5x_3 \leq 74$ (digestive support index from $x_1$ and $x_3$)
- $8x_0 + 3x_1 \leq 53$ (digestive support index from $x_0$ and $x_1$)
- $8x_0 + 3x_1 + 5x_3 \leq 50$ (digestive support index from $x_0$, $x_1$, and $x_3$)
- $8x_0 + 3x_1 + x_2 + 5x_3 \leq 50$ (digestive support index from all variables)

## Step 3: Implement the problem in Gurobi
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams of vitamin B6")
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams of potassium")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="grams of carbohydrates")
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams of vitamin B4")

    # Define the objective function
    model.setObjective(8.98 * x0 + 1.03 * x1 + 7.98 * x2 + 3.6 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(2 * x0 + 5 * x1 + 5 * x2 + 2 * x3 <= 77)
    model.addConstr(8 * x0 + 5 * x1 + 5 * x2 + 3 * x3 <= 64)
    model.addConstr(8 * x0 + 3 * x1 + x2 + 5 * x3 <= 80)
    model.addConstr(5 * x1 + 2 * x3 >= 12)
    model.addConstr(5 * x2 + 2 * x3 >= 13)
    model.addConstr(2 * x0 + 5 * x1 + 2 * x3 >= 10)
    model.addConstr(2 * x0 + 5 * x2 + 2 * x3 >= 10)
    model.addConstr(2 * x0 + 5 * x1 + 2 * x3 >= 15)
    model.addConstr(2 * x0 + 5 * x2 + 2 * x3 >= 15)
    model.addConstr(8 * x0 + 5 * x1 + 5 * x2 >= 14)
    model.addConstr(8 * x0 + 3 * x1 + x2 >= 16)
    model.addConstr(8 * x0 + x2 + 5 * x3 >= 16)
    model.addConstr(8 * x0 + 3 * x1 + x2 >= 12)
    model.addConstr(8 * x0 + x2 + 5 * x3 >= 12)
    model.addConstr(5 * x1 + 2 * x3 <= 52)
    model.addConstr(2 * x0 + 5 * x1 + 5 * x2 + 2 * x3 <= 52)
    model.addConstr(8 * x0 + 5 * x1 <= 37)
    model.addConstr(8 * x0 + 5 * x2 <= 55)
    model.addConstr(5 * x1 + 5 * x2 <= 52)
    model.addConstr(5 * x1 + 3 * x3 <= 53)
    model.addConstr(5 * x1 + 5 * x2 + 3 * x3 <= 28)
    model.addConstr(8 * x0 + 5 * x1 + 5 * x2 + 3 * x3 <= 28)
    model.addConstr(3 * x1 + x2 <= 64)
    model.addConstr(x2 + 5 * x3 <= 28)
    model.addConstr(8 * x0 + x2 <= 56)
    model.addConstr(3 * x1 + 5 * x3 <= 74)
    model.addConstr(8 * x0 + 3 * x1 <= 53)
    model.addConstr(8 * x0 + 3 * x1 + 5 * x3 <= 50)
    model.addConstr(8 * x0 + 3 * x1 + x2 + 5 * x3 <= 50)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("milligrams of vitamin B6: ", x0.varValue)
        print("milligrams of potassium: ", x1.varValue)
        print("grams of carbohydrates: ", x2.varValue)
        print("milligrams of vitamin B4: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```