## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B4', 'grams of protein', 'milligrams of calcium', 'milligrams of vitamin B6']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $8x_1 + 6x_2 + 1x_3 + 4x_4$.

## Step 3: List all the constraints in symbolic notation
- $11x_1 \leq 152$
- $21x_1 \leq 201$
- $14x_1 \leq 160$
- $12x_2 \leq 152$
- $1x_2 \leq 201$
- $10x_2 \leq 160$
- $8x_3 \leq 152$
- $2x_3 \leq 201$
- $12x_3 \leq 160$
- $1x_4 \leq 152$
- $13x_4 \leq 201$
- $19x_4 \leq 160$
- $11x_1 + 1x_4 \geq 32$
- $12x_3 + 19x_4 \geq 37$
- $14x_1 + 10x_2 + 19x_4 \geq 25$
- $14x_1 + 10x_2 + 12x_3 \geq 25$
- $14x_1 + 10x_2 + 12x_3 \geq 25$ (Redundant with the previous one)
- $14x_1 + 10x_2 + 12x_3 \geq 25$ (Redundant with the previous one)
- $8x_3 + 1x_4 \leq 76$
- $12x_2 + 1x_4 \leq 95$
- $11x_1 + 8x_3 \leq 133$
- $11x_1 + 12x_2 + 8x_3 + 1x_4 \leq 133$
- $1x_2 + 13x_4 \leq 104$
- $21x_1 + 1x_2 \leq 63$
- $2x_3 + 13x_4 \leq 139$
- $21x_1 + 13x_4 \leq 181$
- $21x_1 + 1x_2 + 2x_3 \leq 141$
- $21x_1 + 1x_2 + 13x_4 \leq 176$
- $21x_1 + 2x_3 + 13x_4 \leq 134$
- $21x_1 + 1x_2 + 2x_3 + 13x_4 \leq 134$
- $14x_1 + 10x_2 \leq 131$
- $12x_3 + 19x_4 \leq 149$
- $10x_2 + 12x_3 \leq 136$
- $14x_1 + 10x_2 + 12x_3 + 19x_4 \leq 136$

## 4: Remove redundant constraints
After reviewing, we can see there are indeed redundant constraints that essentially convey the same information.

## 5: Formulate the problem in Gurobi
We will use Gurobi to solve this linear programming problem.

```python
import gurobi as gp

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

# Define the variables
x1 = m.addVar(name="milligrams_of_vitamin_B4", lb=0)
x2 = m.addVar(name="grams_of_protein", lb=0)
x3 = m.addVar(name="milligrams_of_calcium", lb=0)
x4 = m.addVar(name="milligrams_of_vitamin_B6", lb=0)

# Define the objective function
m.setObjective(8*x1 + 6*x2 + x3 + 4*x4, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(11*x1 <= 152)
m.addConstr(21*x1 <= 201)
m.addConstr(14*x1 <= 160)
m.addConstr(12*x2 <= 152)
m.addConstr(x2 <= 201)
m.addConstr(10*x2 <= 160)
m.addConstr(8*x3 <= 152)
m.addConstr(2*x3 <= 201)
m.addConstr(12*x3 <= 160)
m.addConstr(x4 <= 152)
m.addConstr(13*x4 <= 201)
m.addConstr(19*x4 <= 160)

m.addConstr(11*x1 + x4 >= 32)
m.addConstr(12*x3 + 19*x4 >= 37)
m.addConstr(14*x1 + 10*x2 + 19*x4 >= 25)
m.addConstr(14*x1 + 10*x2 + 12*x3 >= 25)

m.addConstr(8*x3 + x4 <= 76)
m.addConstr(12*x2 + x4 <= 95)
m.addConstr(11*x1 + 8*x3 <= 133)
m.addConstr(11*x1 + 12*x2 + 8*x3 + x4 <= 133)

m.addConstr(x2 + 13*x4 <= 104)
m.addConstr(21*x1 + x2 <= 63)
m.addConstr(2*x3 + 13*x4 <= 139)
m.addConstr(21*x1 + 13*x4 <= 181)
m.addConstr(21*x1 + x2 + 2*x3 <= 141)
m.addConstr(21*x1 + x2 + 13*x4 <= 176)
m.addConstr(21*x1 + 2*x3 + 13*x4 <= 134)
m.addConstr(21*x1 + x2 + 2*x3 + 13*x4 <= 134)

m.addConstr(14*x1 + 10*x2 <= 131)
m.addConstr(12*x3 + 19*x4 <= 149)
m.addConstr(10*x2 + 12*x3 <= 136)
m.addConstr(14*x1 + 10*x2 + 12*x3 + 19*x4 <= 136)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Milligrams of vitamin B4: ", x1.varValue)
    print("Grams of protein: ", x2.varValue)
    print("Milligrams of calcium: ", x3.varValue)
    print("Milligrams of vitamin B6: ", x4.varValue)
else:
    print("The model is infeasible")
```

## 6: Symbolic Representation
Here is the symbolic representation of the problem:

```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B4'), 
        ('x2', 'grams of protein'), 
        ('x3', 'milligrams of calcium'), 
        ('x4', 'milligrams of vitamin B6')
    ], 
    'objective_function': '8*x1 + 6*x2 + x3 + 4*x4', 
    'constraints': [
        '11*x1 <= 152',
        '21*x1 <= 201',
        '14*x1 <= 160',
        '12*x2 <= 152',
        'x2 <= 201',
        '10*x2 <= 160',
        '8*x3 <= 152',
        '2*x3 <= 201',
        '12*x3 <= 160',
        'x4 <= 152',
        '13*x4 <= 201',
        '19*x4 <= 160',
        '11*x1 + x4 >= 32',
        '12*x3 + 19*x4 >= 37',
        '14*x1 + 10*x2 + 19*x4 >= 25',
        '14*x1 + 10*x2 + 12*x3 >= 25',
        '8*x3 + x4 <= 76',
        '12*x2 + x4 <= 95',
        '11*x1 + 8*x3 <= 133',
        '11*x1 + 12*x2 + 8*x3 + x4 <= 133',
        'x2 + 13*x4 <= 104',
        '21*x1 + x2 <= 63',
        '2*x3 + 13*x4 <= 139',
        '21*x1 + 13*x4 <= 181',
        '21*x1 + x2 + 2*x3 <= 141',
        '21*x1 + x2 + 13*x4 <= 176',
        '21*x1 + 2*x3 + 13*x4 <= 134',
        '21*x1 + x2 + 2*x3 + 13*x4 <= 134',
        '14*x1 + 10*x2 <= 131',
        '12*x3 + 19*x4 <= 149',
        '10*x2 + 12*x3 <= 136',
        '14*x1 + 10*x2 + 12*x3 + 19*x4 <= 136'
    ]
}
```