To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (corn cobs and protein bars), formulating the objective function using these variables, and then listing all constraints in terms of these variables.

Let's define:
- $x_1$ as the quantity of corn cobs,
- $x_2$ as the quantity of protein bars.

The objective function to maximize is given by $4x_1 + 4x_2$.

Constraints are as follows:
1. The cost constraint for spending no less than $26: $14x_1 + 19x_2 \geq 26$.
2. The constraint from the relationship between corn cobs and protein bars: $6x_1 - 9x_2 \geq 0$.
3. The upper bound on spending: $14x_1 + 19x_2 \leq 66$.

Given that $x_1$ can be fractional but $x_2$ must be an integer, we have a mixed-integer linear programming (MILP) problem.

Here is the symbolic representation of the problem:
```json
{
  'sym_variables': [('x1', 'corn cobs'), ('x2', 'protein bars')],
  'objective_function': '4*x1 + 4*x2',
  'constraints': [
    '14*x1 + 19*x2 >= 26',
    '6*x1 - 9*x2 >= 0',
    '14*x1 + 19*x2 <= 66'
  ]
}
```

Now, let's implement this problem in Gurobi Python:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="corn_cobs")
x2 = m.addVar(vtype=GRB.INTEGER, name="protein_bars")

# Set the objective function
m.setObjective(4*x1 + 4*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(14*x1 + 19*x2 >= 26, "min_cost")
m.addConstr(6*x1 - 9*x2 >= 0, "relationship_constraint")
m.addConstr(14*x1 + 19*x2 <= 66, "max_cost")

# Optimize the model
m.optimize()

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