To solve the optimization problem described, we will use Gurobi, a powerful optimization solver. The goal is to maximize the objective function given, which involves the number of fruit salads and pickles, subject to several constraints related to iron intake and other limitations.

The problem can be formulated as follows:

- **Objective Function:** Maximize \(1.26 \times \text{fruit salads} + 7.37 \times \text{pickles}\)
- **Constraints:**
  - Iron from fruit salads and pickles must be at least 69 milligrams.
  - \(2 \times \text{fruit salads} - 5 \times \text{pickles} \geq 0\)
  - Total iron from fruit salads and pickles must not exceed 186 milligrams.
  - Both fruit salads and pickles can be fractional (non-integer).

Given the resources/attributes:
- Fruit salads contain 4 milligrams of iron each.
- Pickles contain 19 milligrams of iron each.

Let's represent the number of fruit salads as \(x_0\) and the number of pickles as \(x_1\). The objective function can be written as \(1.26x_0 + 7.37x_1\).

The constraints based on the problem description are:
1. \(4x_0 + 19x_1 \geq 69\) (at least 69 milligrams of iron)
2. \(2x_0 - 5x_1 \geq 0\) (constraint on combinations of fruit salads and pickles)
3. \(4x_0 + 19x_1 \leq 186\) (no more than 186 milligrams of iron)

Here is the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.CONTINUOUS, name="fruit_salads")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="pickles")

# Set the objective function
m.setObjective(1.26*x0 + 7.37*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x0 + 19*x1 >= 69, "iron_minimum")
m.addConstr(2*x0 - 5*x1 >= 0, "combination_constraint")
m.addConstr(4*x0 + 19*x1 <= 186, "iron_maximum")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of fruit salads: {x0.x}")
    print(f"Number of pickles: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```