## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'milligrams of zinc'
- $x_1$ represents 'milligrams of vitamin B2'
- $x_2$ represents 'milligrams of vitamin B4'
- $x_3$ represents 'milligrams of vitamin C'

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $7x_0 + 5x_1 + 4x_2 + 7x_3$.

## Step 3: List all the constraints in symbolic notation
The constraints are:
- $x_0 + 25x_1 \geq 47$
- $10x_2 + 16x_3 \geq 44$
- $x_0 + 16x_3 \geq 32$
- $x_0 + 25x_1 + 16x_3 \geq 38$
- $x_0 + 25x_1 + 10x_2 + 16x_3 \geq 38$
- $15x_1 + 8x_2 \geq 18$
- $21x_0 + 8x_2 \geq 21$
- $8x_2 + 17x_3 \geq 21$
- $21x_0 + 15x_1 \geq 25$
- $21x_0 + 15x_1 + 8x_2 + 17x_3 \geq 25$
- $9x_1 - 6x_2 \geq 0$
- $10x_0 - 3x_1 \geq 0$
- $x_0 + 25x_1 \leq 165$
- $25x_1 + 10x_2 \leq 79$
- $x_0 + 10x_2 \leq 224$
- $15x_1 + 8x_2 + 17x_3 \leq 113$
- $21x_0 + 15x_1 + 8x_2 \leq 126$

## 4: Define the upper bounds for $x_0$, $x_1$, $x_2$, and $x_3$ 
Given $r0$ and $r1$, we have:
- $x_0 + 25x_1 + 10x_2 + 16x_3 \leq 244$
- $21x_0 + 15x_1 + 8x_2 + 17x_3 \leq 156$

## 5: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 6: Implement the objective function and constraints in Gurobi
We will use Gurobi's Python API to implement the model.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(lb=0, name="milligrams of zinc")
x1 = model.addVar(lb=0, name="milligrams of vitamin B2")
x2 = model.addVar(lb=0, name="milligrams of vitamin B4")
x3 = model.addVar(lb=0, name="milligrams of vitamin C")

# Objective function
model.setObjective(7 * x0 + 5 * x1 + 4 * x2 + 7 * x3, gurobi.GRB.MINIMIZE)

# Constraints
model.addConstr(x0 + 25 * x1 >= 47)
model.addConstr(10 * x2 + 16 * x3 >= 44)
model.addConstr(x0 + 16 * x3 >= 32)
model.addConstr(x0 + 25 * x1 + 16 * x3 >= 38)
model.addConstr(x0 + 25 * x1 + 10 * x2 + 16 * x3 >= 38)
model.addConstr(15 * x1 + 8 * x2 >= 18)
model.addConstr(21 * x0 + 8 * x2 >= 21)
model.addConstr(8 * x2 + 17 * x3 >= 21)
model.addConstr(21 * x0 + 15 * x1 >= 25)
model.addConstr(21 * x0 + 15 * x1 + 8 * x2 + 17 * x3 >= 25)
model.addConstr(9 * x1 - 6 * x2 >= 0)
model.addConstr(10 * x0 - 3 * x1 >= 0)
model.addConstr(x0 + 25 * x1 <= 165)
model.addConstr(25 * x1 + 10 * x2 <= 79)
model.addConstr(x0 + 10 * x2 <= 224)
model.addConstr(15 * x1 + 8 * x2 + 17 * x3 <= 113)
model.addConstr(21 * x0 + 15 * x1 + 8 * x2 <= 126)
model.addConstr(x0 + 25 * x1 + 10 * x2 + 16 * x3 <= 244)
model.addConstr(21 * x0 + 15 * x1 + 8 * x2 + 17 * x3 <= 156)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objval)
    print("x0: ", x0.x)
    print("x1: ", x1.x)
    print("x2: ", x2.x)
    print("x3: ", x3.x)
else:
    print("The model is infeasible")
```

```json
{
    'sym_variables': [
        ('x0', 'milligrams of zinc'), 
        ('x1', 'milligrams of vitamin B2'), 
        ('x2', 'milligrams of vitamin B4'), 
        ('x3', 'milligrams of vitamin C')
    ], 
    'objective_function': '7*x0 + 5*x1 + 4*x2 + 7*x3', 
    'constraints': [
        'x0 + 25*x1 >= 47', 
        '10*x2 + 16*x3 >= 44', 
        'x0 + 16*x3 >= 32', 
        'x0 + 25*x1 + 16*x3 >= 38', 
        'x0 + 25*x1 + 10*x2 + 16*x3 >= 38', 
        '15*x1 + 8*x2 >= 18', 
        '21*x0 + 8*x2 >= 21', 
        '8*x2 + 17*x3 >= 21', 
        '21*x0 + 15*x1 >= 25', 
        '21*x0 + 15*x1 + 8*x2 + 17*x3 >= 25', 
        '9*x1 - 6*x2 >= 0', 
        '10*x0 - 3*x1 >= 0', 
        'x0 + 25*x1 <= 165', 
        '25*x1 + 10*x2 <= 79', 
        'x0 + 10*x2 <= 224', 
        '15*x1 + 8*x2 + 17*x3 <= 113', 
        '21*x0 + 15*x1 + 8*x2 <= 126', 
        'x0 + 25*x1 + 10*x2 + 16*x3 <= 244', 
        '21*x0 + 15*x1 + 8*x2 + 17*x3 <= 156'
    ]
}
```