To solve John's cereal mixing problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of servings of cereal A as \(x_1\) and the number of servings of cereal B as \(x_2\).

The objective is to minimize the total cost, which can be represented algebraically as:
\[0.45x_1 + 0.55x_2\]

Given that John needs at least 400 units of iron and 450 units of fiber, and knowing the nutritional content of each cereal serving, we have the following constraints:

1. Iron constraint: \(25x_1 + 20x_2 \geq 400\)
2. Fiber constraint: \(30x_1 + 40x_2 \geq 450\)

Additionally, since John cannot buy a negative number of servings, we have non-negativity constraints:
\[x_1 \geq 0, x_2 \geq 0\]

Thus, the symbolic representation of the problem is:

```json
{
  'sym_variables': [('x1', 'servings of cereal A'), ('x2', 'servings of cereal B')],
  'objective_function': '0.45*x1 + 0.55*x2',
  'constraints': [
    '25*x1 + 20*x2 >= 400',
    '30*x1 + 40*x2 >= 450',
    'x1 >= 0',
    'x2 >= 0'
  ]
}
```

To solve this linear programming problem using Gurobi in Python, we'll write the following code:

```python
from gurobipy import *

# Create a new model
m = Model("Cereal_Mixing")

# Add variables to the model
x1 = m.addVar(lb=0, name="servings_of_cereal_A")
x2 = m.addVar(lb=0, name="servings_of_cereal_B")

# Set the objective function
m.setObjective(0.45*x1 + 0.55*x2, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(25*x1 + 20*x2 >= 400, "Iron_Constraint")
m.addConstr(30*x1 + 40*x2 >= 450, "Fiber_Constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Servings of Cereal A: {x1.x}")
    print(f"Servings of Cereal B: {x2.x}")
    print(f"Total Cost: ${0.45*x1.x + 0.55*x2.x:.2f}")
else:
    print("No optimal solution found")
```