To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints.

Let's define:
- $x_1$ as the number of fruit salads,
- $x_2$ as the number of pickles.

The objective function is to maximize: $1.26x_1 + 7.37x_2$

Given the constraints:
1. Iron from fruit salads and pickles must be at least 69 milligrams: $4x_1 + 19x_2 \geq 69$
2. The constraint on iron intake from both sources is no more than 186 milligrams: $4x_1 + 19x_2 \leq 186$
3. Another constraint given is: $2x_1 - 5x_2 \geq 0$

Now, let's represent this problem symbolically:
```json
{
    'sym_variables': [('x1', 'fruit salads'), ('x2', 'pickles')],
    'objective_function': 'maximize 1.26*x1 + 7.37*x2',
    'constraints': [
        '4*x1 + 19*x2 >= 69', 
        '4*x1 + 19*x2 <= 186', 
        '2*x1 - 5*x2 >= 0'
    ]
}
```

Next, we'll write the Gurobi code to solve this problem. We will use Python as our programming language since it's commonly used with Gurobi.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fruit_salads")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="pickles")

# Set the objective function
m.setObjective(1.26*x1 + 7.37*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x1 + 19*x2 >= 69, "iron_minimum")
m.addConstr(4*x1 + 19*x2 <= 186, "iron_maximum")
m.addConstr(2*x1 - 5*x2 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.x}, x2 = {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```