## Step 1: Define the optimization problem
The problem is to minimize the objective function: $5.42x_0^2 + 3.59x_0x_1 + 4.15x_0x_2 + 5.43x_1x_2 + 7.08x_2^2 + 6.25x_0 + 7.4x_1 + 6.3x_2$, where $x_0$ represents grams of carbohydrates, $x_1$ represents milligrams of vitamin B5, and $x_2$ represents milligrams of vitamin B4.

## Step 2: List all the constraints
The constraints are:
- $20x_0 \leq 281$
- $20x_0 \leq 98$
- $1x_0 \leq 92$
- $16x_1 \leq 281$
- $13x_1 \leq 98$
- $11x_1 \leq 92$
- $8x_2 \leq 281$
- $5x_2 \leq 98$
- $10x_2 \leq 92$
- $20x_0 + 8x_2 \geq 61$
- $16x_1 + 8x_2 \geq 87$
- $20^2x_0^2 + 16^2x_1^2 \geq 91$
- $20x_0 + 16x_1 + 8x_2 \geq 91$
- $13^2x_1^2 + 5^2x_2^2 \geq 26$
- $20x_0 + 13x_1 + 5x_2 \geq 26$
- $11x_1 + 10x_2 \geq 18$
- $1x_0 + 11x_1 + 10x_2 \geq 18$
- $-6x_0 + 8x_1 \geq 0$
- $20x_0 + 5x_2 \leq 95$
- $20x_0 + 13x_1 \leq 98$
- $13x_1 + 5x_2 \leq 91$

## 3: Convert the problem into Gurobi code
We will use Gurobi's Python API to model and solve this problem.

```python
import gurobi as gp

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

# Define the variables
x0 = m.addVar(name="grams_of_carbohydrates", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)
x1 = m.addVar(name="milligrams_of_vitamin_B5", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)
x2 = m.addVar(name="milligrams_of_vitamin_B4", lb=-gp.GRB.INFINITY, ub=gp.GRB.INFINITY)

# Define the objective function
m.setObjective(5.42*x0**2 + 3.59*x0*x1 + 4.15*x0*x2 + 5.43*x1*x2 + 7.08*x2**2 + 6.25*x0 + 7.4*x1 + 6.3*x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(20*x0 <= 281, name="muscle_growth_index_carbohydrates")
m.addConstr(20*x0 <= 98, name="kidney_support_index_carbohydrates")
m.addConstr(x0 <= 92, name="immune_support_index_carbohydrates")
m.addConstr(16*x1 <= 281, name="muscle_growth_index_vitamin_B5")
m.addConstr(13*x1 <= 98, name="kidney_support_index_vitamin_B5")
m.addConstr(11*x1 <= 92, name="immune_support_index_vitamin_B5")
m.addConstr(8*x2 <= 281, name="muscle_growth_index_vitamin_B4")
m.addConstr(5*x2 <= 98, name="kidney_support_index_vitamin_B4")
m.addConstr(10*x2 <= 92, name="immune_support_index_vitamin_B4")
m.addConstr(20*x0 + 8*x2 >= 61, name="min_muscle_growth_index")
m.addConstr(16*x1 + 8*x2 >= 87, name="min_muscle_growth_index_B5_B4")
m.addConstr(400*x0**2 + 256*x1**2 >= 91, name="min_muscle_growth_index_squared")
m.addConstr(20*x0 + 16*x1 + 8*x2 >= 91, name="min_muscle_growth_index_total")
m.addConstr(169*x1**2 + 25*x2**2 >= 26, name="min_kidney_support_index_squared")
m.addConstr(20*x0 + 13*x1 + 5*x2 >= 26, name="min_kidney_support_index_total")
m.addConstr(11*x1 + 10*x2 >= 18, name="min_immune_support_index_B5_B4")
m.addConstr(x0 + 11*x1 + 10*x2 >= 18, name="min_immune_support_index_total")
m.addConstr(-6*x0 + 8*x1 >= 0, name="balance_constraint")
m.addConstr(20*x0 + 5*x2 <= 95, name="max_kidney_support_index_carbohydrates_B4")
m.addConstr(20*x0 + 13*x1 <= 98, name="max_kidney_support_index_carbohydrates_B5")
m.addConstr(13*x1 + 5*x2 <= 91, name="max_kidney_support_index_B5_B4")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Grams of carbohydrates: ", x0.varValue)
    print("Milligrams of vitamin B5: ", x1.varValue)
    print("Milligrams of vitamin B4: ", x2.varValue)
else:
    print("The model is infeasible")
```