## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents 'milligrams of calcium'
- $x_2$ represents 'milligrams of vitamin B6'
- $x_3$ represents 'milligrams of magnesium'
- $x_4$ represents 'grams of fiber'

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $5.58x_1 + 8.21x_2 + 1.51x_3 + 6.53x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $18x_1 \leq 445$ 
- $12x_2 \leq 445$ 
- $20x_3 \leq 445$ 
- $26x_4 \leq 445$ 
- $18x_1 + 26x_4 \geq 58$ 
- $18x_1 + 20x_3 \leq 246$ 
- $18x_1 + 12x_2 \leq 219$ 
- $12x_2 + 26x_4 \leq 253$ 
- $18x_1 + 12x_2 + 20x_3 \leq 331$ 
- $12x_2 + 20x_3 + 26x_4 \leq 125$ 
- $18x_1 + 12x_2 + 20x_3 + 26x_4 \leq 125$

## 4: Consider the variable restrictions
- $x_1$ can be any real number
- $x_2$ can be any real number
- $x_3$ must be an integer
- $x_4$ must be an integer

## 5: Write down the symbolic problem representation
```json
{
    'sym_variables': [
        ('x1', 'milligrams of calcium'), 
        ('x2', 'milligrams of vitamin B6'), 
        ('x3', 'milligrams of magnesium'), 
        ('x4', 'grams of fiber')
    ], 
    'objective_function': '5.58*x1 + 8.21*x2 + 1.51*x3 + 6.53*x4', 
    'constraints': [
        '18*x1 <= 445',
        '12*x2 <= 445',
        '20*x3 <= 445',
        '26*x4 <= 445',
        '18*x1 + 26*x4 >= 58',
        '18*x1 + 20*x3 <= 246',
        '18*x1 + 12*x2 <= 219',
        '12*x2 + 26*x4 <= 253',
        '18*x1 + 12*x2 + 20*x3 <= 331',
        '12*x2 + 20*x3 + 26*x4 <= 125',
        '18*x1 + 12*x2 + 20*x3 + 26*x4 <= 125'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=-float('inf'), ub=float('inf'), name="milligrams of calcium")
    x2 = model.addVar(lb=-float('inf'), ub=float('inf'), name="milligrams of vitamin B6")
    x3 = model.addVar(lb=-float('inf'), ub=float('inf'), type=gurobi.GRB.INTEGER, name="milligrams of magnesium")
    x4 = model.addVar(lb=-float('inf'), ub=float('inf'), type=gurobi.GRB.INTEGER, name="grams of fiber")

    # Define the objective function
    model.setObjective(5.58*x1 + 8.21*x2 + 1.51*x3 + 6.53*x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(18*x1 <= 445)
    model.addConstr(12*x2 <= 445)
    model.addConstr(20*x3 <= 445)
    model.addConstr(26*x4 <= 445)
    model.addConstr(18*x1 + 26*x4 >= 58)
    model.addConstr(18*x1 + 20*x3 <= 246)
    model.addConstr(18*x1 + 12*x2 <= 219)
    model.addConstr(12*x2 + 26*x4 <= 253)
    model.addConstr(18*x1 + 12*x2 + 20*x3 <= 331)
    model.addConstr(12*x2 + 20*x3 + 26*x4 <= 125)
    model.addConstr(18*x1 + 12*x2 + 20*x3 + 26*x4 <= 125)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of calcium: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Milligrams of magnesium: {x3.varValue}")
        print(f"Grams of fiber: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```