## Step 1: Define the symbolic representation of the problem
Let's denote the number of servings of grains as $x_1$ and the number of servings of vegetables as $x_2$. The objective is to minimize the cost, which is $0.40x_1 + 0.60x_2$. The constraints are:
- Iron requirement: $30x_1 + 15x_2 \geq 100$
- Fiber requirement: $5x_1 + 25x_2 \geq 150$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## 2: Convert the problem into a symbolic representation
The symbolic representation can be written as:
```json
{
    'sym_variables': [('x1', 'grains'), ('x2', 'vegetables')],
    'objective_function': '0.40*x1 + 0.60*x2',
    'constraints': [
        '30*x1 + 15*x2 >= 100',
        '5*x1 + 25*x2 >= 150',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 3: Implement the problem in Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='grains', lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='vegetables', lb=0, ub=gurobi.GRB.INFINITY)

    # Set the objective function
    model.setObjective(0.40 * x1 + 0.60 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(30 * x1 + 15 * x2 >= 100, name='iron_requirement')
    model.addConstr(5 * x1 + 25 * x2 >= 150, name='fiber_requirement')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: grains = {x1.varValue}, vegetables = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```