## Step 1: Define the symbolic representation of the variables
The variables are 'mechanized infantry companies' and 'logistics companies', which we can denote as $x_0$ and $x_1$ respectively.

## Step 2: Define the symbolic representation of the resources/attributes
The resources/attributes are given as:
- $r_0$: mobility rating
- $r_1$: defensive capability rating
- $r_2$: logistical capacity
- $r_3$: fun factor

With their respective values for $x_0$ and $x_1$:
- $r_0$: $x_0 = 5$, $x_1 = 6$
- $r_1$: $x_0 = 1$, $x_1 = 10$
- $r_2$: $x_0 = 6$, $x_1 = 9$
- $r_3$: $x_0 = 2$, $x_1 = 4$

## Step 3: Formulate the objective function
The objective function to minimize is $4.6x_0 + 8.11x_1$.

## 4: Formulate the constraints
The constraints are:
1. $5x_0 + 6x_1 \geq 17$
2. $x_0 + 10x_1 \geq 21$
3. $6x_0 + 9x_1 \geq 20$
4. $2x_0 + 4x_1 \geq 19$
5. $-3x_0 + 10x_1 \geq 0$
6. $5x_0 + 6x_1 \leq 64$
7. $x_0 + 10x_1 \leq 79$
8. $6x_0 + 9x_1 \leq 36$
9. $2x_0 + 4x_1 \leq 64$
10. $x_0, x_1$ are integers.

## 5: Convert the problem into a Gurobi code
We will use Gurobi to solve this linear programming problem.

```python
import gurobi as gp

# Define the model
m = gp.Model()

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

# Define the objective function
m.setObjective(4.6 * x0 + 8.11 * x1, gp.GRB.MINIMIZE)

# Define the constraints
m.addConstr(5 * x0 + 6 * x1 >= 17, name="mobility_rating")
m.addConstr(x0 + 10 * x1 >= 21, name="defensive_capability_rating")
m.addConstr(6 * x0 + 9 * x1 >= 20, name="logistical_capacity")
m.addConstr(2 * x0 + 4 * x1 >= 19, name="fun_factor")
m.addConstr(-3 * x0 + 10 * x1 >= 0, name="mixed_constraint_1")
m.addConstr(5 * x0 + 6 * x1 <= 64, name="mobility_rating_upper")
m.addConstr(x0 + 10 * x1 <= 79, name="defensive_capability_rating_upper")
m.addConstr(6 * x0 + 9 * x1 <= 36, name="logistical_capacity_upper")
m.addConstr(2 * x0 + 4 * x1 <= 64, name="fun_factor_upper")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Mechanized Infantry Companies: ", x0.varValue)
    print("Logistics Companies: ", x1.varValue)
else:
    print("The model is infeasible")
```

## 6: Symbolic Representation
The symbolic representation of the problem is:

```json
{
    'sym_variables': [('x0', 'mechanized infantry companies'), ('x1', 'logistics companies')],
    'objective_function': '4.6*x0 + 8.11*x1',
    'constraints': [
        '5*x0 + 6*x1 >= 17',
        'x0 + 10*x1 >= 21',
        '6*x0 + 9*x1 >= 20',
        '2*x0 + 4*x1 >= 19',
        '-3*x0 + 10*x1 >= 0',
        '5*x0 + 6*x1 <= 64',
        'x0 + 10*x1 <= 79',
        '6*x0 + 9*x1 <= 36',
        '2*x0 + 4*x1 <= 64'
    ]
}
```