## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fiber', 'milligrams of vitamin B3', and 'milligrams of iron', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ = 'grams of fiber'
- $x_2$ = 'milligrams of vitamin B3'
- $x_3$ = 'milligrams of iron'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $4x_1 + 5x_2 + 6x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $5x_1 \leq 55$
- $3x_2 \leq 55$
- $8x_3 \leq 55$
- $3x_2 + 8x_3 \geq 13$
- $5x_1 + 3x_2 \geq 14$
- $5x_1 + 3x_2 + 8x_3 \geq 14$
- $-10x_1 + 7x_3 \geq 0$
- $-7x_2 + 9x_3 \geq 0$
- $5x_1 + 3x_2 \leq 49$
- $3x_2 + 8x_3 \leq 51$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin B3'), ('x3', 'milligrams of iron')],
'objective_function': '4*x1 + 5*x2 + 6*x3',
'constraints': [
    '5*x1 <= 55',
    '3*x2 <= 55',
    '8*x3 <= 55',
    '3*x2 + 8*x3 >= 13',
    '5*x1 + 3*x2 >= 14',
    '5*x1 + 3*x2 + 8*x3 >= 14',
    '-10*x1 + 7*x3 >= 0',
    '-7*x2 + 9*x3 >= 0',
    '5*x1 + 3*x2 <= 49',
    '3*x2 + 8*x3 <= 51'
]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="grams_of_fiber", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="milligrams_of_vitamin_B3", lb=0)  # No lower bound specified, assuming 0
    x3 = model.addVar(name="milligrams_of_iron", lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(4 * x1 + 5 * x2 + 6 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(5 * x1 <= 55)
    model.addConstr(3 * x2 <= 55)
    model.addConstr(8 * x3 <= 55)
    model.addConstr(3 * x2 + 8 * x3 >= 13)
    model.addConstr(5 * x1 + 3 * x2 >= 14)
    model.addConstr(5 * x1 + 3 * x2 + 8 * x3 >= 14)
    model.addConstr(-10 * x1 + 7 * x3 >= 0)
    model.addConstr(-7 * x2 + 9 * x3 >= 0)
    model.addConstr(5 * x1 + 3 * x2 <= 49)
    model.addConstr(3 * x2 + 8 * x3 <= 51)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of fiber: {x1.varValue}")
        print(f"Milligrams of vitamin B3: {x2.varValue}")
        print(f"Milligrams of iron: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```