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

Let's denote:
- $x_0$ as the number of 'military intelligence companies',
- $x_1$ as the number of 'mechanized infantry companies'.

The objective function is to maximize: $2.69x_0 + 8.87x_1$.

Constraints are as follows:
1. Deployment weight constraint for military intelligence companies: $0.52x_0$
2. Logistical capacity constraint for military intelligence companies: $0.16x_0$
3. Deployment weight constraint for mechanized infantry companies: $0.27x_1$
4. Logistical capacity constraint for mechanized infantry companies: $0.03x_1$
5. Combined deployment weight must be at least 45 metric tons: $0.52x_0 + 0.27x_1 \geq 45$
6. Combined logistical capacity must be at least 41: $0.16x_0 + 0.03x_1 \geq 41$
7. Constraint on the combination of companies: $-2x_0 + 10x_1 \geq 0$
8. Combined deployment weight must not exceed 117 metric tons (repeated constraint, but it will be considered as is): $0.52x_0 + 0.27x_1 \leq 117$
9. Combined logistical capacity must not exceed 57: $0.16x_0 + 0.03x_1 \leq 57$
10. Both $x_0$ and $x_1$ must be whole numbers.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x0', 'military intelligence companies'), ('x1', 'mechanized infantry companies')],
    'objective_function': '2.69*x0 + 8.87*x1',
    'constraints': [
        '0.52*x0 + 0.27*x1 >= 45',
        '0.16*x0 + 0.03*x1 >= 41',
        '-2*x0 + 10*x1 >= 0',
        '0.52*x0 + 0.27*x1 <= 117',
        '0.16*x0 + 0.03*x1 <= 57'
    ]
}
```

Now, let's implement the solution using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="military_intelligence_companies")
x1 = m.addVar(vtype=GRB.INTEGER, name="mechanized_infantry_companies")

# Set objective function
m.setObjective(2.69*x0 + 8.87*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.52*x0 + 0.27*x1 >= 45, "deployment_weight_min")
m.addConstr(0.16*x0 + 0.03*x1 >= 41, "logistical_capacity_min")
m.addConstr(-2*x0 + 10*x1 >= 0, "company_combination")
m.addConstr(0.52*x0 + 0.27*x1 <= 117, "deployment_weight_max")
m.addConstr(0.16*x0 + 0.03*x1 <= 57, "logistical_capacity_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"military_intelligence_companies: {x0.x}")
    print(f"mechanized_infantry_companies: {x1.x}")
else:
    print("No optimal solution found")
```